{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"end_to_end_project\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)\n",
    "\n",
    "# Ignore useless warnings (see SciPy issue #5998)\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import tarfile\n",
    "from six.moves import urllib\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    os.makedirs(housing_path, exist_ok=True)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "URLError",
     "evalue": "<urlopen error [WinError 10054] 远程主机强迫关闭了一个现有的连接。>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mConnectionResetError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1316\u001b[0m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[1;32m-> 1317\u001b[1;33m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[0;32m   1318\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1228\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1229\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1230\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1274\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1275\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1276\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1223\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1224\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1225\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1015\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1016\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1017\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    955\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 956\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    957\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1391\u001b[0m             self.sock = self._context.wrap_socket(self.sock,\n\u001b[1;32m-> 1392\u001b[1;33m                                                   server_hostname=server_hostname)\n\u001b[0m\u001b[0;32m   1393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mwrap_socket\u001b[1;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[0;32m    411\u001b[0m             \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 412\u001b[1;33m             \u001b[0msession\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    413\u001b[0m         )\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36m_create\u001b[1;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001b[0m\n\u001b[0;32m    852\u001b[0m                         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"do_handshake_on_connect should not be specified for non-blocking sockets\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 853\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    854\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mOSError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mdo_handshake\u001b[1;34m(self, block)\u001b[0m\n\u001b[0;32m   1116\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1117\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1118\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionResetError\u001b[0m: [WinError 10054] 远程主机强迫关闭了一个现有的连接。",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-bd66b1fe6daf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfetch_housing_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-3-46cc27e809d3>\u001b[0m in \u001b[0;36mfetch_housing_data\u001b[1;34m(housing_url, housing_path)\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhousing_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0mtgz_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhousing_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"housing.tgz\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m     \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murlretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhousing_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtgz_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m     \u001b[0mhousing_tgz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtarfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtgz_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[0mhousing_tgz\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextractall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhousing_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[1;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[0;32m    245\u001b[0m     \u001b[0murl_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msplittype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0mcontextlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclosing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m         \u001b[0mheaders\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    523\u001b[0m             \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    524\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 525\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    526\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    527\u001b[0m         \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    541\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    542\u001b[0m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[1;32m--> 543\u001b[1;33m                                   '_open', req)\n\u001b[0m\u001b[0;32m    544\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    545\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    501\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    502\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 503\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    504\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    505\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1358\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1359\u001b[0m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[1;32m-> 1360\u001b[1;33m                 context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[0;32m   1361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1362\u001b[0m         \u001b[0mhttps_request\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1317\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0;32m   1318\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1319\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1320\u001b[0m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1321\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mURLError\u001b[0m: <urlopen error [WinError 10054] 远程主机强迫关闭了一个现有的连接。>"
     ]
    }
   ],
   "source": [
    "fetch_housing_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "\n",
    "def load_housing_data(housing_path = HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, 'housing.csv')\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
       "       'total_bedrooms', 'population', 'households', 'median_income',\n",
       "       'median_house_value', 'ocean_proximity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20, 15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2450cc99320>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y='latitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2450ccaa278>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kczPyRK1Aa00/TFmveWy2R1xt+tjm6VVGYZzwwUGf9/cHtIcxlhT0w4y9bsBeP+RyzcMYG/1JMnj2pKGUzkNGwuSgH9IbRLyx1WEUxSzVfJq+jZzRyJqXO1FKY5uSKwsejmkcKz+ejPHtBDFxptDj0JhlyKk0iGvn/7ykIeaGjh5XiKno+ygoePw81c4jTDLutkd874M23fD0615YqdEseyxWHD5/pY7vmMcM7EkjJaVgseJwtx0gpcKQgpVanjhOycNPtnl2lVGGZqs9ojOMGSVZnj8YBGTKZqni4NgGAqbGHI4n5OM0Y/cwpCUjOsOYb7x3yNEg5MZen1eu1nimWeW1a41pOGwemdZowJ84AcSx08rVxRJWR07XvVY/uxP/ZOjocYaYJus4WXpcUFDwaHhqnce0KkcK1pedUxUiX1kW/Jc/c5XnlypIKSnZ5lQH6qyS0JJtcrnhYQiwrfyEkSiNyXyD2SzZHA1jgiQDDU3f5gdhimVIHKVRClqDhIpj4Vr3nNfEmAP5OuQ9Y+7aku/fPuTrb+9y92iIYwqEYdAdhWwuBfSjhGbZ4ZUrDVzLuO9ZbEOeadxtQ3J5HN6azcWcN3T0OENMRbluQcHj5al1HkmmiNMM0xDYWLy0VuIH28Nj15QMePXaGr0gY6sTstbwuNsJpob1rJLQqZx6N6QzjDkaxjTLNtu9kLpv0RklRGlKkioQeVVXGOciiyXT5GgUU3VNYjNPmLeCGNsUVH2LZsnOmxLHxnwy3yNOMg76IUIIskxx86DPv31zhx9uTZ5LA4oPjhJ2e7kE+0azzCjKePFSBduSWEIeC08tVxy2uwEizqZhslNlRGZyIecNHT2OEFNRrltQ8Ph5Kp3HpD9hpxvSGcW4luTqUpmPjoYMIvABacBq06FasVkqWXi2QX1GdmS15k7j9Wq880+VOhavdy2D9brHrdaQqwv+9ATSGSWs1z0yrdnuBNimJEkVN/YGaDSXGz4LnoVjmlRdyWLFQaWKesXmmYUyh8OYYZTv1Ou+xd1OQKoUO90QwwDXMumGCf/uxzu8vzOc+x3cbkV85lLAqOJyc7/PUiXPgWRKY0iBbxnTOR+Qu53lijOtADuPcT5vw+OjlhYpynULCh4/T53zmBg+25SsNzwO+yEH3Yi6b3N9qUKcZQgEQZyCFuy3A15criKlmMb6ozTvgZBCMAwT2kFCnOSOY63mYRn3gmBa5NfZ1j1DFiQJsVIYQqDH99nthViGxDQEEuiEKZeqzvRek5yCaxn4jjmtqLrbyTW4LMPAtfKu7ktlh9YgpB8khHOKAAAy4HZrhGXZrDd82oOYhbKDb5uEY6dhmRLPMvBsKy/F7UfTHoy/KsZ5Xs5pkkuJ02zaHFmU6xYUPFqeOucxa/j8cWgp05qvLJZ4b7eH1jAIU7TQRBl0g4S/+OiIl680WKm4CHJjP6k2mmhb2abBsm9NDexJQzbJGwzDhN1uiNDkk/4yhRSAyKuSlNbYlsEgTKj7FtcWSveVAk926kmmjp1+bEsSJVl+vSHwXOPMOeWdQcSOPeKFlTIrdY8404zi/ERT863pzHY47iD+KvRShGHKURAxiFKssRNbqjiUxsn9kmPw3k4/X68UvHy5VoSsCgoeIU+d8zhp+JYqDtvdkLJj8vdevcJ/uLHHO1tdKp7FWsPjUsVnmGaUbYM77YD1hsfquKrIMiWrVRfHNqbGfRgd34HPJoWDJJmGvMpuvptPUkWWaZJUU7YMDFMwDFOUzjuu55UCn/YsDc9iO85IEoUlDT67UmXzYMBWf/7xoxfDZUNQ921c26BhGggp0EqTKn1fBRjkO33D+OR6KZTSfHjQ54/f3uNwEGFLwVeea2KZBlvtgMXxbPnDQYwhYbXqYRuSziih6lqFAykoeEQ8dc7jZIWPaRj89ReXaY1iVqoezyz5/OGbO6zUXbJMgBB0gphnl8tonZ9UJppQhhAYRu4mZhPYJ3fgk6RwmGYIDWX3Xj+HZeYVS4sVh71emJ9AENNu9Ys8i5SS1zYaWKakUba52w74u68K/s0Pt9juKWZdyJILZc9ive7RHiU0PZt+nKHSe5pUwPTek8T+VieYOouHSXTPhpmAafjt5OlqHmGScftowL/+3l1cS1LzLHpBwv/9vTt89YVlFssuR8Mol0YxJa5p0B4lXK57JElW5DwKCh4hT53zgPsrfOCehtOVhk9rmHC3HdKPYgTwzGIJ2zTQOg9XzRrAU6cOnkBKgWsaGIa8L9yTKU17lOQ5kBmNp4d5lsln1z2btZqLbcAvv6z5cLfHjd0eWkCswHUsSq6Naxp4psSxDRpl5777bCzkQ6Qmif2TneCz+Z0HMSukmKl7RQbtUcJS2cGzzVNLaie5qiTJAEHZtfjoYEjFNRmGir1+THuU8NylCq5lTF2E0pponPswxPy+nIKCgovzVDoPuJc3mFdy+qXri3ibHbpRTBArnl0ukaSatbpLPDacs9efps80y6xA4axq7Kw0ycnE9HmN27xqJSkFi1WHXpiwVHEZRYp6xWOrNeRoEFH2bF5dr+GN+1bMU0bdSimQWlxozvk8ZoUUEXDrYAhaY9sGpsg78Ncb3qnDoya5qopvY5mCMM5wbYPDQYQU4NuSmmNx1I9Za7hcqnns90LiVKM1rJ7yuyt6PwoKHo6n1nnA2f0AX3lukVgp0kSxP4xyraheSJIpyo45FTzcao0QUpypzzRvYt5Em2rSb+KYFx+AdNYuOtOasmPxxWea7HV9pGiRKc1LK1XutoZ8dNDljbstqo6FkIIf73R4cbV239wNeDSd4LNCikGSstUJyJSi5puY0uTd3R5Hy2Vqnk3Ds3BsA1vKac5nsgZTCr764hJff2uP9iDCMg3+7itr2IZBqjQlW1J1LDKlafg2l6rudBDVZmtU9H4UFDwinmrncVbJqWVKpBJs9iI8yxjrTaVs9SKchuRgEJFmimGc0iw5rNS8++4xkS4/6aD2+xEbCz7xOBy014toDWNW6x6mFOcyzA/qoJ7O8FCadJx8z32R5s27Hd4/CEgzhSFihGFTso/ohikbDZ/LDf9Y2Ow0+fSHMbpKa1rDGGkIDCkZhhn9KKLuWThm3uX+9bf3aI7LlF++XKPu29M1bHcCqq7Nf/LaZSwhuNMOcC0T0xBUXRPbNFgqO+z2QwwhaI8SHMvAkOKvTHlxQcFPAucPWP8EMrujBo7tqJXShGlGNt5tQy4zgtJ8eNjnoB+x2w15/6DPW1sdbh0OCJPsvl35xEFJMSnLzY3YZPiUY0o2mj5CwObRiDhVD6xcmnVIJcfEMkTuSNS9wtyJsY0yRRBnLFYdGp5NexTxo60elhRUPRchNG9tH3Fzt81ee8RuL+T7m21G43keEyby6ZNP2O9HhGOZ9tl1TeTjTzIpbQ6SlDhVVG2Tkmni2yZxpvBtEy2gHyUYBjR8G9eU/HirS5reS/VPvhXXNFlvlvny9UWWKg6Nko1jmVyquhwOYzzLoOxZ0+9GaE79XRcUFFycT8R5CCGeF0KEQojfmfnZfyWEuC2EGAohfk8IsfC41zExsEmmGUYpSaZZqeWx8c3WiO1OwE4vZBAmQG4c62WLw0FCmKRstkbYwqAfZ4zidK7xn4Smbh8N2e4E3D4akkzKXnXeR+FaBhvNEpeqzrQR8CzunZju5SCU1lONqwmuZXBtocTluseVhs/1S2U8w8SQmopnkyqNY1skGbSGCf0kxTMNQLPdDY45gVn59Ipr3eewwiRjszXiTmvEZmt0n2ORUnB1scRyxaXimFxf8vnMWoVG2ebqgs9nVqs0PQsQ+HZeUuvaJpnSxEoda+6s+Ta2mcvDuJbB9aUyV5ulPIFvyrnfjRbM/V0XIauCgofjkwpb/Rbwl5MXQojPAf878HeA7wNfA/4X4Fce90LmVV5NYuOebSGB7W7IqgbDkGwslGj1Y9phykLZwbPz0ItrGSyW7GOlvFM0TDa4QuSvT+YRcvlz41zVSxfJQZimZH3BzyuVMsXVSz5136E7DNEIojiZRLPoDBNutYY4lsGCbx8L6ZwV4kMdnx0SpXlV1dU5iW9DSi7VXDqjlIWSRbOczwjphxlhpqj7FmXXBA2DKMYQAlvKs0OMhrwXejpD0deyZCHVXlDwiHjizkMI8StAB/g28Nz4x/8A+AOt9TfH1/wPwDtCiIrWuv+41zRbZTTbtQ1Qci1WgNW6hzuWUV+sOhyOItCaMFYsVuy8T8GU9xn/Sf5ko1mayp8HcTbdCT9Mo91ZarTzkuizDvJas8Rv/GLCP//j94iCBNC8eqWW62GNUvpBwqWKzeEg5rlMw9gPnuWwTht+tVhxpsnqycnBMSUlx6PuZ0SJmlZWTU5jwzjljTsd3t7pIoAXVyqkWmPL/DcUxOl0tsg8h/kgpd7Z33VRtltQ8PA8UechhKgCvwn8EvDfzPzV58idCQBa6w+EEDHwAvC9Off5deDXATY2Nh7pGucZSVPmDWcTA/PMYpmjYUziKYZJhm8ZpJlmpXK/8Z9NXD/KnfC8/o6zkugTo6mU5vmVGr/6c1f5xjt73Njtsd1LaJYEL67aBEnG4TCh4phstkesL/j5WNizkuYKtNLc6YxyyXop0Vpz0I+m1VsnTw62aZBkeThJSoEzVuSd5Eak1FiGQZQqbh8OubLgE4/nwkM+1XDeyea07+YkhWR7QcHH40nnPP4Z8Nta6zsnfl4Guid+1gUq826itf6a1vp1rfXrS0tLj3SBp+VBZg2Q75i8fnWBq80Szyz6GEKyVHY4HMZzY/2PK9Y+O9P8PEl0yE9CgzDmw/2AXqRplHzqnoVAc9ALQSmWqjaNsoNjyWP3OJk03+2FdEcx3VFMexTy4cGAjw4H9IOE5Yo7/Tw4uzhhliRTtIYJNdeh6tp5BVYv5G57hG8bXF30Wa+7WIbEnhPiOytpP3vNeb6rgoKC03liJw8hxCvA3wRenfPXA6B64mdV4LGHrOZxnp2r75hca5a41RpSX7KncuvzegdsQ7Jay43p7NCkMMnY7gTHJvE97O73vEq3eb9KhFKaRskmcTIGUQYiz1vUSy5oyYJvI4QgSVMGYZKPttWag16E75ooDZuHQ77x3j4f7HbY6wWYhmCx4vHS5QaGIWiWnalz+DiDn5TWhElGL0ynJ4WSY0zzLROJk2GcstMNiNKMfpDRrNh41v1d69PeGuvivTUFBQU5TzJs9VXgGrApcoNSBgwhxGeBPwK+MLlQCHEdcIAbT3B9xzjPjIl5cusnjdBpQ5OU0tw+HNIJYsS4wzpJFc9fqjyUTtR5k+hawErVxTIlYZSSZnnuwZSwcanMF67USNOMnV5AFOcVVP044oPtHt1hRM23+NmXVlj2fW63hvzed2/xo91gqpvVcNr0QsXlhstJSd8HOeXJzn+xYtMdJcjxCWK54tAaJniWPCYZf6WecTiMCeKU7U7AdmdEEGcoGIe+oNwwjzn0iUTKXi+iPYpZqZ2/t6agoOAeT9J5fA34VzOv/wm5M/kNYBn4jhDiF8irrX4T+NdPIln+cXiQwT6rgz0ZNwtWXHP63v1+xLXF0jT+fxbznNJ5dvaGEDimwYuXKryz1eFWKwAEzy6XeHm9zkajxDfe36fuWbRGCbcP+vzZh0fc7cRMAnJ//HaLn3uxySiKeXvGcQC0I/jurV3+669sYJnyvt38aU755PPUPWsallusOEgxYpTkJdGGFDR8i91+noAfxCm9MGa/F7NadzkcRARJ/n2uN/xpXw0ZbHeCaW/NTidg82jE5YZ3b/56QUHBuXhizkNrPQJGk9dCiAEQaq0PgAMhxH8L/EugCfx74Nee1NoeltNCMcA07n5qeevH4CyndJ4EvNKaFM3lBZ8rCyVSrVmuuBz1Y7a9EcsVl7W6y+GtI27u9dmecRwAAwV/fvOIZtkgmXP/QQitIKZR8e/bzc+rcJr3PHGqWKvfG6zl2WZewkueL0kyfa/xL9VY0kAIjYI83JZptNbT2fFb4/DgXi9io5kXAVxtluiHuerufeXVBQUFZ/KJyZNorf/pide/C/zuJ7Oah+dkKGbSYDgZTZtkau7JxDAEy1WH9jC+F56ZmRx4FufueTjlvYYULJccGmWXqmNyOIjoBgnhFgCYAAAgAElEQVS9MGG17rFSd/N1GpIgTUnn3UiAJSWC7L6BU5YEz5LHHOnku5lX4TT/edR0FjzkZc23D4fTaqvFcl4erbTGNAVRmpFkcNALiNN8wNZ63UVrQIMznorYGsbsdgM2miVSVXSZFxQ8LE+1ttWjYrYM9uQOOknzXXQ0npMxG0q62ixhG5IkU1iGnA6ZehAfR6jQEHkoyDAFvgWbBwNMS1DzbC7VXGquxXLJoR0muKakZJmM+xrv46UrDUZpm83uvfOHA7y6UeeZxSpKaTa7I7JMkaHJMk3Vs+47Lc17HkF+IlEi742xDYltSq4seNM+j8G4es238pLelZpDnGaUahZLZYcvPtPEtQy2OsG0EXOp4rDVDjjsh3SDlGbZ5m4nKEp1CwouSOE8PgZpqoiVmqq/zttBW6acxtMnoZpJ6MY2JFebpQv3eTyocums5jcpBY2SzXdvHfHjzS63OiEN3+KVDYvPrdWwTQOkoO5aXG6U+C++dBVp3OG7t3vT0FXNhFeuLfD59QUyBc6dFh+1UxTgSMgUfOuDA5ZKLktVhzDRjOKEvV7EF68uIEUu8Z5lanpamn2eJFOgjw+eMmT+Hn88ZlaSO8HLdY9hnHLYj/JZK8ClioMUAt8xp45pECa0Rwlxmp+UBHB1wT+zSq6goOB0CufxECilORpGvHm7zTDLqJgmrz6zQNk2UUoTpxm2aUxPBLO9GP0w4bAfoeFjNaedVrn0oOY3pTR3WkPe2uqQClgsmRhScNiLuLk/4FLN47UrDaSR389ZrfKZ1Spff3eHtzYPMU2L51aqLFVLVB2Ta02fd3e6TIJbPQX/380OHx72+OXPrbNUdfmp9Tp136E1SPjwoE/JtYjSfKjT6jivMXmeJFNsjZPasxL363Xv1NNWd6ycO3nO1ihmqeJOv5fJrHlDCBzLYLFksNePaZbzsFpRqltQcHEK53FBwiTj1tGAP35rlxs7XUqujSkFu4OILz2zQKIUu0chzZI9nYw3KRHd7gR8tN8nzDKu1H1qvn3uHe+808TJyqWzEumT9ySZ4s7RiMNBQs1zWCg53G2N2O9HxKnmUiVvdlws2ez180FLliF5aW2B1UYJIQTmeK5GZ5Rw2I3ojuL71rvZUXyw1wUBh/04r5qqOtw+HOKMEmxL0nBt7rRG0/LkyeApYKpCbAiB0got8vLb7W6AiDOM8WlFizyktlr3OOhHKK2J0zw8NXlmy8yv9WYGbO318xJf2zLQar7USUFBwekUzuMCKKW5sdvjR3fa/MmNA3zHoOxoXMvkGzf2+fxqhcW6T9k2iTLFet3DNOXUqN/tDPg3b27TGcUgJD//fJNXNhosV52p/Mk8J3FeKY3zNgpqA4TUKBQGEseSuJbJat2l7Fr0w4TtbsZazaUdJIyilF6Q8trGAp1hxHdvd9jtBiRpRpxlpHM6sxVwMAypdB0+cnsorbnW9DCk4HLdxXMs0BwrT1ZKo1Q+Nna/F06/j0bJJpmRJtHkjsS18vdMhkRdrntEaYbWUBqfAicNhEprOsNwKpViylz2RI2d4+fX60XIqqDgAhTO4wJEScaNvQFqvCuOUsGNvQHXFkv0RxH9NGVBa2zLIFG5bhPkRr0zDPnDN3Y4HEaUHAtDCL53q8UwyrDN3HjXPItukEx1sNbqHrYhH3iamDCbeJZSEI+lUmZ31JYhudYoca1Z5vbRkF4QkSm4vlhmtepyZyynLjSsN31Wqm4+2yRRbLUD3t/r0x0lmFIyTBNuHPSJ7j94IIA40VQ8C8c08RyDrU5EmipawxQ7VlQcg3Qstz6K8kY/rTXb7QApwbGMabntXi+XY583qneSL1E6D2WdHDnbGkb85UdHvL3dZb8T4Dgmy2Wby80yn1ut4duCvW5I1bUKB1JQcE4K53EBMq3JlCJMU1xL0h4muQDgIKLkmGzuBxjSoOnbSCmnRlvoXNY9yTR1zyZWijhRRGlGGKfsdEJ6QUIvTGn4FosVF8sQJKni2mLp3BPwJoZ0tqR1uZpLnrvynkDis8sVfuklxfc3WxwNEuquyeUFj61OgGubXKo4vLc3oHW3w+WGT8O3MA1BphVJmhEkGb0wQWpJxbW5vJBy8yA51guy4MDzl6pcbZZ4dqlCN0wYxSlSwjBO6Iaa93djnrtU5sPDAbu9EK0UwzijM4zxnHxmSLPssNcLaZRsVqr3T2tEgSEF63UvVzY+IauvMvjOB4fc3OsTphmjVNOPAnphSpxoHFNwfbHMfi9kreZS8e3H+U+ooOAnhsJ5XADXNGj4FptHI15aq/ODO4cITCSav/XyGraZS2dsJ4rXNhrTXawWTCuuOkGCY0qEUKRa43kmt1sDgijjTjvgZ68vMoozFsv2dFytFII4zaZTCM+Kz88raT15UnEtg5fX6nmoTEDJseiFMW/e7bFmSX6806NsCYJMEyUZW52UpbKD7xgc9mJaw4RBmNANEgwk6/UyVTuiNwqxLIOqa7O+WOZas4xhSIZxBlpgSUmUZXz3g0MGUYLvmKzWPD7Y6fHnt4/Yagd0RxFCGrxwqcKXn12kM4yJlaLVjwnijMsNfyonkqSKrXGeYxLOsyx5TFa/E8d0RglCQJLm7k1riJOM260B7+x08b18jvrd1oi///rGdKRwQUHB6RTO4wKYpuQLVxrcPBhSlyY/++wyi75NkGZcrnlImfdqJKnKJ9rNxNyrns1Xnl3kT98/4HAQoxWs1VySJOPb7+0RZoowTFit2jy/Up+qz0opqPsWb2y2GUUJvmPxhRnHdDJHkml9X0nr3LyHyJ/HG+dO+lGGEJoP9gd8/3aLTMNa3eXK5zxMw+BwEGGHkkbZJtnKGEYJoyDCtgx6o5gsy1ioOLz+7BJCmHimwVLVRQDbnRFLNZc4Uby306czyh1BP0j4t2/e5aODPludJB+SZUDJlhz1QwyhWKr5/PSVBsIw0Dof1bve8LhUddnvR3PDebPhO1sIDAGZ0gzjXEK/P8zf1w0UhoBLUnB10eXGXp//8O4ef//VK9h20fNRUHAWhfO4IEsVl198YYmDQQgI9noB/a5ipxexUnXzCiFD3rcrrrgmi2WX//SVywyShEXf4c3tLv/Ht26y2817DyTQ+8EW/9lrgiAp8fxyBUMIbu73eWenR5hkmDIP23z5mSbp+FQxu/O2jfMNTUrS3NgaQiAkDKOELFO8u9MjUSA1DKOMf/ejHZ65VGW16pKNJUHqZYeXJPzBm33aQ8U05XEU8NbWJn/jp5b5pZdWydJcIsQxDa41XH68nedLlAIpJWGc8sZ+i6MRZOPnVynEqaJiK9642+XyMMW3TZ6/VOWFlQrDMJ2e4s7qsr+XB4GX1+uMxr0gg2HIINIIoUkzsGwIE0U/TPEsg2GcEWQZNoXzKCg4i8J5XJBJzsC3TaI0QyD47EqVMNOkqWanG/LKev2+XXE/TFmruaRK4YQmwyjhB7cO2elmCHLjmQF3OwmHnRHXGmXaw4T2KOIb7+5x0I/oRymjKOW9nT5KZNRsm0bJwbPN6c57ueI8cGjSZB75as2lPUroBRHv7vZxJARJllctJYowzWgnGS/bBkGiCNM8x9MoWdzc7zEKZxzHmF4Gv//DfRq+xWdXG2g0CM3buwP2egFaKSqeRS+I6YcJ/bHjAKYCizEQZBAnirJr0hsltIYxaaqOjeo9q8t+tg9mseLgmwYrVYdRmlIPEwaBYpSkpApMoemMEhxD4FkSzygcR0HBgyicx0MwEdUL0wzTkFRcK593oTRBkiGNebvifCzrj+520WhSpUlSPe12tgXE44rXMFFsNH2STPP9zUP+7MMWrWFAkOR9Dxqw/wJqJZeffqbBRqOEZxtkmWa7kxvcqm8RJxmZ5r6hSZOS3rJr4ZoGSZrx/FKJ1jA/FQyijMsNl6NBjCMhShSOZeCYBkmSEsSKj/aH9I/PvZqigB/cOiKIFOsLHo2yx3rDwQBMKbix06czjDGERkg4Jss7xjGhXrZZKLk0yjZBlDCMUq4v35Osf5CK8KQPpuJarC/4bHVGXG2WCaOM9/b6OGhEmhHGGVpIliouv/D8UhGyKig4B4XzeEikFLimgSnldPer0JhSYks5d1estCbKMhwz15aqeSaWgETnYSLItaE8x6QfJfTCjLe3urSGMWECIOkFMUrDVmdAP9G8++0eriUou3mfxs89v8yLK3UAXNtkGN2f75jNCYRpxm4vwpCC1ZrH5zdqvL8zoD1MAMVy3UcL2OuGeI6R7/wluKbkFLsPQGsQcacV0BklLNdiwjjX8Vqp+zimgWcLPjocYs65iQFYpoFjGQihSFKNb+dSL7OcZ2jX5He1WvewLAPHMGmlMXXPZDdOWCi5XFvyeOFSlReXq5RskzRVmOaTHrJZUPDponAeH4PTNKbMcUfzdidgGKWYhqTuWfx4q0c/SIgtg7pvcf1SjSsLXTaPcrkSV8Jy1UYKwUcHQy43PEqOZKni8NF+Dy0lQQyGgA/2R1xbMtjtDBnEikEEtoQ//NEO//0vfYaff/EScZqhVJ6nmLfunU7AndYIhWK14uFa+XREzzColC0Gw4TlqkeQKjzLoOHZNEomwzDl8xs1bh4M6J7S45GqPH9gm5IoVmweBVQ9kysLft4ZrsZ9GHO+V0tA3TVJUsVHBwGrz3n89NUFfNtkuxNweSxpMu1Kf4CkiFIa1zL4/OUagyAliPMZ7QLB+oLHgmey0x3y1t0Wb24f8dJag1987hLL9aLqqqDgNArn8TE5a/c7+S+tNbvdAEPClWaJ/X7EQT/merPCP/pr1/nOB/tsHoZYUvPcco0vPbNInGnCVHOp5rNWDdjpDOkGCRJwLJASPjzsMwzvhbsCBTudlH/+x+/gO5KK556qGutaBgslm1tHQ5ZKDgf9mJqv6AQpr19bwHVMNttDLCHZKNt0g5TuKOGwn7BcdWiWlpDAH/xwi/3g+HdSd+6dhCwp0WjiLCOIBbvdgKNBTMmS3G0HDObovRsSyr5JyTF4dtGn6tlIAXGWNyqmWZ77mKfbdVZ3vhCCa4sl6r5JnCq6o5j3dnv8Pz/YoT9exzc/7FN3tvn2Cwf84//4s6w3Sh/zX0hBwU8mhfN4BJymMWWbEt/Jez+2BjGubWCIXEajN4pZrrp8udpkseISJopBlHBtoYSUkkzlc0Aavs9WO2Cz3SdIMiqOIBMSCzgcJLn0xvhzTcA085zFdz444h98+RpVz57b66FU3txoGxLPNqi4eRJ7ueRQKdmMwpT2MCFLc0n0hmcRxhlXF3yQgs3DIdeWa/zqLzgMg4RRlPDBwZDWKMF3La41SggpqbsWsVbESUaUZBwkKZ0w4dYoojfPcwBKARpsma9tpxOQKY2UsODbuJaPgPvGy86rPJvtzrcMwdEw5uV6jRsHQw77IX9x62DqOCZ0IvjhZps/+vEOv/rlZ4ocSEHBHArn8Rg4qTFlWwZCCGquST/KiOMUhGC94WOZkuuLFRxTstvLS2d7YUKcKg76EYMo46W1Cl+6Xuf/+tYtLEuSakmSxARxhyjOK5Ng7ER0XvK62x1x86DPcjVjueqitD6W+xiOO9uFgO1uQN2zEUJS9gw2j4YcDCIypdCIXOyxG9Gs2th2Xj2WoTElrFR80jIcDSJqZY/OKObqQolMwd3OiDDNSJWiHaSsVW02W0O2WwGtQcTwlIR7xculUTzTYrsdMEgyjnohJc/i0I4QIu9PqbgmwyAhUBntXkzZPz4rZLXmHv89mAZLZQchoOqaGAZE80YhAolShFFRtltQcBqF83gMnBxupJRmueJgm5KGlGhhsVbz8J1cvM8cJ9hrrsXb2102WyO01ggh6Y4iMi348rUGX3puiTc2OwRRQtkx+fkXL3HQGfH9zT5DlTsPoaHu2xhItroRIAmSlCuN0rHZ6ofjUmLXMvBtgyBJWannvRyDKCRJNY5lUnNM1moeSZJ3xG8eDRnGKX/5wRG1ks2lqkO3HyOA5arPWtWnHyY4Vt7lfn3R59ZhwHfe3+f3f7BPN2Tu6FrInV7NhitNnyTRhHGM1BqlBUdBSD+KqXgO/Y8yNpoeh4OIQZBLnkQp/O3PXWJjsTzt+YD7y3k922St6qKB3rCM50ja0f2Zl7pnUi1ZRdluQcEpFM7jMTCbSA/jBC3gyoI/Hbl6MjfSLNvs9UL2ehGNksUwcTjoRRgCFsouR/2QP/+oxUrd5asvLWMKzZ12LuS3VXZZXSjxpzf2iJXGtSxWax7PrVQxBaRKs9+L+cL6va70JFMkmeJSLS/HzcuMBYtlh51OgIEENGmasRunaJEXFEuh+eCgz93WkKNBjGkadEcpscpYKrs0Kw79KOFOb0R7N2BnGPKtH494ZzvmYH6EaooBNL1cxsUxTWxTM4pgqxfQGiYY5ErAyxWPlZpHexTyzk6fsmNRcQ082+L3fnCXf/hz1yk55nSOyryCBtsyuLpQYqcT8NJKnUHQojfj0ZoO/I3PrfG3PruKacqpNHwhmlhQcI/CeTwmXMvI50908mzyfj+6L8E7G6fXSlP3LaqOx0EvJlOKKAEhYoJEUXIEjiXojlIcy6Dh27y4UsZ3DN7Z0fzCCyuUSxZRkLDdi2gNY0BwdbGEKe8NSgqTjJ1OwF4vwjYFyxUXIXK9J1MI3t3tY0mJbRjcbY/yWeALHluHI/63b77PTjciTqDmCr5wNeWFlSqtYUrFyavEbh0O+H9/uMlWKyOY98WcggW4luBwmJAOYjZbElvmIcC8X0WgtcIxE9YXPG4dDBmNT2CJ0pCkCC15f7fHZy/XpyN9XTm/oMF3TH766gLtUcx6w2ez1UdpzUuXq/y9Vze4tlBFCabz6D/O4K6Cgp9ECufxmJh0cXu2cWwi3iTBe3JwU5xm7PUjqq7JUsXhB3faOIbEdyRxmnFzb8iPNts4loVhSF5erzIM8zCMKQ2WaxZKKQYixUCM9bRM3t/t8zPXm/lQpfFnmlKwVnfZ64XcbQdcbnisVPPS4mbJJs40vSCiH6asNzxaw5Df+bMP2WpFOCZIC0ax5s3NFusLHlcXPJarNn/50T7/8lubdE5r/ngAcaLJdIrvWcSJYm+QYQpwXYFnmqRKkSpNN4iJsxTXtkhThV92OBrErC6XuNz0WKo4xxojTyvnrbgWr20s8Pm12tS5mGbeAAr3lHkfJIVfUPA0UjiPx8SDBjNlWpMqhWXk+lO2adAs2SRKs1C2eW6pRD/MaI1itMqT54lSZFmAYQjuHA34jz6/ytWFMssVG8cyeGe7w42dHlJIfFdyNDQYxZrbrSG2aXC54RMkKcMoQ401oCquweXxLh0BJcfEyTL6Afi2xLclB+2IbpgiRJ5XSVMINcgQBsOE1zcWudsK+D+/uUn//rlQ56LigzRNpBakmcA0c0FDaeYlz1XPpBekmFLSjzJSJVj0LXpRys541vmXn11gECr2uuF00uDkpHDaJMbVusduN0Rm+bTCS9W8S31WmXfe76+g4GmncB6PiZNJ85PaS7PChLYlaXgWnm2yXvcYxilHw5jeKOboVkgax+z2A4TOw0v5VLyM1ijiixsLXFly+c7NHvvdCCE1japDa5Rxc3efyws+PzKgG6Qc9CISral7Fr6dlxDnQoW5Uc2UpjuK+dF2j/d2uvh2PqzJcyRaQZzBaKZCaqDh7Z0ujZLJN98/fGjHYQANx2aUKRKlsC2J0FDxDGqeyShSdIMESwqaFYfVcSNmliqqvkUvSPnCegUhDFZr+TTE2ZPC7GCok+GnueFFKbCN+SoBJwUm5zmlgoKngcJ5PCZO6z6fhKxmhQmjJGM7zqYzQFrDmKpr8OM7Q+60A3qjiGGQEsT5CFYpIdLw4WHM+4e70890gJfWfII4Y7c7oD1KGcSarV7Efj+fS24YBlrAKE7H0xAVd9ojTCOvyjocRBz0AxxT5qcQ0yCIFM82fXY7MbN5b09Aa5Twxp02Nw/ntJqfAxdYX7BYqJSoqpRukBKmKRKDL1yts77gcWOvR5IqFssOzyyWQQgMA456EWXPxhSaa4tlWsOY55ZzAz45KSRjx3Fa+Oms8OKDtLPOOx64oOAnkcJ5PEZO6z6fFSb0bZNMa6I4wzIl/TDh3d0+H+532WyPGEQJYZKiFUSTG49zCifbJCLgh9sj1kojugFUPEHVt7FNybvbXV5eK7NW9ymbLr5jcaczwrUFJTfXczrsR4RJhtSCZtllqz0kiDMEGZudEEOQey/Ak9DwTQwpMThd4+osfvGay1c+s07Ntzjoxry4VqUzjLjTHtEeJLywUiFONV95domjUYTKYJQkHPYSqr6FMPMFhZkm09AdJtw6HPDCapU0vbei2fCTlII4yp2KI40zw4tnqQeczFkVOZGCp43CeTxm5iVr7+sDyTSGIYnijDfudLh12Ge3H+NKg0wpOkFCdEpD3Ty2h3meoIRgEEQYBnRGCV9/a4+ya3Gl4XN9tQIKri1W2O2GlGyDvV7Eh4cDNg+67PUDRnFKreShs4wk0yzWDOJORgKkCtIsxTLzWeDG3eA+Z3YWL1/yePnqMlXPxjENVhoGrmFgmyaXqj4/+1wJW0oO+hG3WyMavs1RP+Z7t9qkWYbvOthSMIgyNpolGp6NkILbRyOU0shxziNTevpdpyqXiYnTPN+zOp4Rf1Z46rRk+4NyWgUFP+kUzuMTYF5Ia7nisN0NEEKzUHJ4b7dHP0jpDuPxGNfz378qIAWOhoo4iRnGYEm4sTMgzuBPdJuyDV96to5tGaxqhw8PYuq+yVt323zv7nB6rzv9ABuolwyaZZulSsx2P59BIk2DL19f4G//1CofHo74wdbwtCVNMYCfu1bhylJe4nvQ61Dzba41PWKlEAgqnkmSav5/9t40SLLsuu/73fv23LOy9qqu3qd79hlgMMAAArEMRJiUCdKwIJMS5RBIiwoqzCD9waJML6JCkinJ9BIhmZRpfiJpKcJcLBIgQFEEsRAgtgEwC2a6Z+u19qzcM9/+3vWHl1VTVV3dXdVT3TPdk7+Iju7MV/nezezKe945/7P0ohClFHGUkHcMen6EY+q03ZQkTUiEhheGLLYFBVNy/1yZ1W7AgnBwDI2uF3G16XJk2I798sYAx9JYqOXRpdh3eGrP93ETTWvEiHudWzIeQogngJPAZ5VSAyFEHgiUUjcpBRuxye6QyOa4WkPXcMOYSs5ClxLI019KCGWCSPdnQ0wdwigLa7WHUkSUgrstthSE8PlzbS41PM7OZEZEi4MdhmOTEOgMEibyivumSkwWfc5MlvmpDx1jvlLCsXQsTeNffPZFXtjdJXEXVRtiIQjjFKWyvlmnpwpEqeLccpfjkwWmSw5LTZeVjs+x8RyDMEURkiRZLYwfKaRK2eiHWWaZLglSxcX6AF3TyJs6lqERxylLbRfHlCw2XdZ6AcXIYKKYYDvmvsJT1+NGmtYmIzF9xL3MgYyHEGIK+CPgPWT72GngAvC/AT7w84e9wHuZHSGRFDQty7rKmzqWLkksnYpTJFWCtVaPCxsR/j7Ou7GtWlrjWm1kO+fWAybyfZpeyvdXr7/xZ11tE6JU8Vfum+aDZyY4MVnF0CR+lDBbzfOLP/IALy62uVJv872LDa62stfpJvTDLFNMAS03wtR95qp5CpbGVMlireMTpwpSxSCMea3eJ00UUZJypObw6mqfMEnouhFBnBBrClPP+oVJmVXHO4bEDRPqg4C057PU9uh5IS8stpmr5ijnDCSCl1d6PDJX3vIUbnWTv5HRGYnpI+51Dup5/O/AKlADrmx7/neBf3VYi3onsn3GxmTJRmowWbBRQlGwda46OjnL5fxaHzfen0BtkLU3J72xAVnvhix3rtdxKuNITedHHp/nB05OUXAMlBCoRBGkCRfrfdpeSNONma0WuX9ujI89eJSX13t86fwar9f7mFpKyRJEKLwwJoxTGj0PKRy+8XqTvCOxDMkgjLjSGNDqh1QLBs1BhBfGjBdNCrZGGKdECuI4IVCZjjFRsrCNLKzmhDE9N+L1xoBL9R6a1Kg4OgVLx9QdDE3QDSPcOOH4eOGGabz7/X/brXGMxPQR7wQOajyeBp5WSrXEztju68DCoa3qHcrmeNvxosViy2Vj2HDww2enCJNxVjsB51ebPHulw9WNHle7Nw5iRYBMs4aDNzIebTciuEHAsWTA+05N89SJSUxLZ6njY+uCK80B1ZzJ6+sDZioW5rDteXMQcmaqgJSSgiX4vW8vsdIesDQYrncQ4Q06TNXyBInC1iTzYzlOTRZpuxFumDBdsYkTaA5CNroBHzhdw9I08pbOxQ2XWt6g78VoumCqZDNetLB0SZwoGl7IatujkrOo2CbtIOLyhku1YDCWt7FMm+PVPAJYamdpyYe5yY/E9BHvBA5qPByyEPhuJmBfEZURN0HKbOb2makSJ8Yz/8IYttoIooRTkwWePhvxvStNvnhuha9dvLFInQAlG47VbM4v+bh7/MwNolUA/NRTC3zskXnumyzxar1Hmqa8sDyAVFG3TZRStN0IXZP4YcxGL6Ca0xFSMl528MKYtV3XWAtB77mcmcxjWSZBoqgWLKSWhXxmKjZBpLJRuVECCDphhCYkpyYLFGyNvpegG4KeH7Pa82n1Ix6aLaMbsNo2iKIU05SMGwaXNga8vq4TRIp3H62y3POJk5S1bsBCLYeuHd4mPxLTR7wTOOig5q8Af2fbYyWE0IBfBL5wsxcLIX5HCLEihOgKIV4RQvxX2479DSHEOSFETwjxkhDixw64tnsKKQWWkc3xhmGDwCSl0Y9IlOTMVJWPPjDHqbHrh1gmbfjI2SIff3Cadx0d5+kHawdex2NTOolIudho8WevXuIzz17lxaUNNJHVkPT8gChJGAQxhiZY7gQUbZ2cZTBRMLlSH3C1Eex57uV+1qfKDxOSNKXnRViazpFajiTNBj+NFUyePD6GGyYUbQNNF1RzJguPYfQAACAASURBVDnTYGE8hxsklGyDvGFgawIvjLN27lFM0w1Y67hcWOtT0LPGkl4c8acvrXF5o4+hSQwtm26YKnVom/xmCDJKFIMgJkrUDTO40jTTdtL0Fkv0R4x4Czio5/EPgC8LId5DVtD8vwIPAmXgA/t4/a8AP62UCoQQZ4EvCSG+R6aj/A7wo8CfAD8M/K4Q4phSav2Aa7yn2BRekyRlpeOjlMLUJKYjmSo5PHJkAj9eZbF77WvXfah3IzQRcanZ58WL/QNf/9m1mGfXFq95/v5Jm0fnKwSxYrZik7MtjlRz3DdZ4MhYDsfSCcOE3/76a3t6O5CJ55cbA+xexMJ4HkMTlHI6UaJoDyIMXTDuOJRsHVMLOD6R5+R4gbWejxvEFG2NOFEYmoauwUTZYbnr4gYpVxp91nsBQqUgBJZh8dJaj7G8zfFajpyhYRiSuUqOxaZHz4u2Rtsehi6x3wyukbA+4m7lQMZDKfWSEOJh4GfJCpptMrH8/1RKrezj9S9ufzj8c3K4jrZS6vPDY38shBgMj71jjcdu4VVKEAjCYdO+jhczV7V5OBxjtdtkL9ni2WWfsXWf5iEnUZ9b9zm/vsq4Aw/PVfnhx+bwooQj1RyWoSGF4JtXl7naufF5Wl7Ew7Uix8ZyXGl5PF6wOD6eJ4oSljs+JUtHDOtgpBDoWhZQ0qSkaBqMFy3G8gZSCvregJeWuqy2PDb6Ab0gxUuzgsma7aKQ9PyEJFXMjOVYbQfMlGzmqw6zFQdDkwcyHDfL0rpegeH214+E9RF3Kweu81BKrQL/6FYvKIT4NbLQlwN8D/gc4AHnhBCfAP4Y+BEy4/T8dc7xM8DPACws3Ls6/XbhNU2zzrtRnDJTsgnihDVTo9ELeXXdvWH88bANxyYKCFJ4Zb1H5ZUNHj8xxtmZEmGsCOKYZ15u3PQcYQC2IVmYyNMPsgFVtpHNH6mlKQrQpEAp6PoRG70QQxOcmCgggfGCST+IaQ4i4jhlLG/ywtU2UZpuZZopoOODRna+IE64VHdJEsV4weSxhepWeHC/abuH4TGMhPURdzM3NR5CiB/Y78mUUl/Zx8/8fSHEzwFPAR8mKy5MhBC/BfxbMm8mBD6llNpTDVZK/QbwGwBPPPHEPRso3i28VnMGKx2fIElRCppuSJQoNCkOLF4dyvoATUAviDm31mG+atMahJyZKqEEnJjLwXPNG54jSEAqWO+EIGGl5SOq2fup9wKOj+exDZ04SRkEMRNFk7Jjbm3sC7U8aZKy2PGxDcFazyNWiiSFcJvRFICQEEYpHTekltd4aK7M8VqethtRso19p+0elscwEtZH3M3sx/P4EtnN2+Zv9OZmvfsxZPvJTVFKJcBXhRA/CfysEOIl4F+SGZPvAu8G/kgI8UNKqWf3c857kd1VzJqUvGuhiqFLvCB7PFGyGOtY1Dse/q01tr1lyrZEZZPTEQgqRZuNfriVJfb0mSP8889dq5dsxwdWej7vtiX3TZVYavlcaPQxhMSxJe5yhCZgsmRhaDrmcP6JJNt0HUNntmZjmToaMF92yFmCVl9dc++uS8gNixIRGlMVB9vSh6L2jbvvbucwPIY4TgnTlPG8ycYgPFBrlBEj3g7sx3hMbPv3e4FfBf4Z8PXhc08Bv0Qmpt/K9U8CJvAVpdQzw+e/LYT4JvAx4B1rPOD6wmusSRxDY7Kc411CULE0Pn/+5mGiN4skKz4s5UAoSJWk6GjcP1tCKUXHjXj2aoNnLqzyet3lsWnBs6vXdw4VULIlSZLyjQsbxElKYxAykTd55kqLy/UeYSpwDMlTpyf5zx6bJ0nVlq6yOZN8vprjwnoPxzR48miNZy636XsRegIFCwqOga1rzFZynJoscXy8QNeNKZoJcninv1+DsOkxhFGCkAI1bL64X4+h7YZ8f6lDkmZe4wOzpSyTbNTGZMRdxE2Nh1Jqa0cSQvwT4OeVUv9x249cEEKsk3kOf3y98wghJoGPAp8l0zg+BvwE8DeBNvAPhRCPKaWeFUI8DnwQ+LWDv6V7j72EV8vQODNd5PxKFyXg+GSJv53T+e3vrt22dTw6oTFVMujHkjRJ8JWOUHB8usDZ6QpRnPLKept/9802377U3lPA34uvv9rlSsOl2Y5p7iqd14CKpYgQfPPVOtN5g8ePjfP4kSo5S8fQJGmaeRmGJjk6kWdhIsd9U2UaAx8hBEIIVrsBJdvg4fkKkyWTrpcwCGOCJGW+msvE8n2GkKQUVHLGDgPw0Fx5Xxt/HKd8f6mDrUvs4UCul5a7vO94DandmuEY9dAa8VZwUMH8AWCvOMQScPYmr1VkWVr/huwG9jLwC0qpPwQQQvwy8HvD/ll14H9WSv3pAdf3jkFKwX3TJXKWzsnJPB0vpuJM8YXvr7F8m8JXz9UTqGe16hrwyIzOY8fGuH+2hC5lNh2x1eF7lzPDcbO+Wpt4wGvXUfUToB9AJZf1vFrtBiw2B6QJHJ/IEw8bZgkBa92AyaJNywt5YLbCSs/n7HQR29BwNMmFRtarfhAmlJ2sz9V08Q1dY7/dddM0K4o8WsshhcgmOw51k5tt3mGakqQK29Sz1vFS0HYDljsDJvMOtn2wr+Qo1XfEW8VBjceLwD8SQnxaKeUBCCEc4H8aHrsuSqk68KEbHP/XwL8+4Hre0diGxonxAonKIxR0/JCKw20zHttJgBdXBpyczHFmYhbHNlhsujwXCWKVCWJSZGJ4RPaLZgMHrzTJ0u7qbootodkLuNL00KTg5GSeRi9AKVgYz4FSnFvpMlWy0HTBw3NlTk0Wt1Jwa0WbZ640qTgGtqFTzRlsDEJsI5uuaGpyX7UZUZISxlnR4ubPDIL9aR6mlGhS0PECWoOYcyttXlzq8I0LTabLNv/pI7McGy/s63MZpfqOeCs5qPH4WbKw05IQYjON9mGyveSvHebCRuyP7SGtOE7RHQ06BxnLdIvXJTMKQQxrgxDDT2i6McemHHQBsYJom9QxV4AP3j/N559bpbHNuJns3e9mNylZq/lBnPLaWpc4UUyUHLp+hC4lUkKCQg1zOwxNoEu5o3bDMjXmyg6Gnj2va5JmP+BSc4Ac6hg3u3P3o4TltsdaN6A5CJmpOOhS7Fvz0HXJAzMlvnB+jbW2y8srXRaqBYqOQRQnfP77K3z6fcf35YGMUn1HvJUctEjw20KI48BPkoWpBPD/AP/2emm1I+4MaapY6/ksVAs8v3qTyrzDuB6ZcL4wYXN6soipawwqEc9cSvjQ2RpfOt8gGBqP6Rz83Y/eT8E2KJgm55eboMFKvc9yF8J9Jlv3Q1jveEwUbdb6Ls2BjxsopssWmhC0BxHTZZupooVlaARxumMjjeKUtV6AJgSmISnbOo1ByNGxXDbDPYy50hhwbCyP0ARCZRs0vNFfbLXjY+mShVqO1Y7HlYbLfNVhpuLs+26/6Bg8OFsirwuWuj4TFYsoSrENgyCK6ccx9j6+mqNU3xFvJbdSJOgyrLEY8fYhUQpL05gdK5Ojc92WIIeFCfwnD9V4+oE5cpYBQN4ymKk4/MLHzvJXH22ytBZg6gkffWCOei8miGMWJopUihZXmx5RotGJe/QG+5uAHgNrrZBj43kKpkmrH1IrZJ5CkircKMELYzYGIWmqqObNrY00TRXrvYCZsk3LjQiihEU/YqJgkQKvr/dY7we4QcylxoDJokVrEJGQfa4TBZPJsk2SpDimga5lNSY9L2K24mwVGe4HTQhsQ6dgmxhC0HNjDF3iRxG2qVPQ9/e13M9AqhEjbhcHHQb1yRsdV0r9wZtbzohbRRMCy9R58mSVzz6/iNvb34Z8UJ6cgqceWuDhI1WeXJik6UZvzGJX2Wzwgm3wgeMzhPMJiYJa3mS128bUJV6UUrYMvtlq4MUJJdvCNgIutfe33gFQ77mcmMyjS/CTGE2zCJOEak7HMbNfaSHYUYG0GeIp2AY5UydRCi+IUcDFeo/LDQ8BbAxCTF3ihjGozOMo5DXOrXZZbLvoUjKT2hRsY+t9b3ol+0VKwWzFIYpTHjlS5duXmji6xnjJ4ocemrlhyGp3ZpVtaMxXshG+ppTo+ltRLjrinchBPY/fu87zm1/TUZrHW8TmXagfFfnA6Sm++PIqfU/hH7IN+dYarAyu8OhcjZypYxra1p0vwJnpIj0/Jgqz+onpksVq18c2JBKoODorXZcoTihaBmXHpDXQmApdWm66L/3jaiOg0V9hsmxTdUyOjuc4PlGg7FjkNIHQoGhZpJthp5StjrVbhi5RGLpG0dT49qUGUZKQtwzG8iZtNyZKEnKGji4FXhTTDxK8KEGlcKHeJ2fqjBVNjlRzDMKYvKkfeArh6akix8bz/NWzUwQqpWQYNzQce2VWASy3PQZeSKRSTtSKVArWvtcxYsStclDNY8dtjRBCBx4H/hfgvz/EdY24BWxD49REkU8+Pk97ENALQgSKgRfy/NqNJwUehKt9+Onf+h6/9EOn+NR7jrMwlm2gG72Anh8jgPGiRX54hw9QzZmcX+vRdCNUIpgftgVRaTbLvJw3KDsJiRQkYUwQKlauM2fE0LKRuN9fGqAz4JnLLebLJkKAUikoxbuOT/D+01PkTI1eEKMJQawUUZxi6FlNRyVnsN710RAIISnZOm0vYr3jM1Yw0TVBolIubfhUcgYdN8QyNC5v9Dk9VYA+GEKw0Qu3dI+DTiG0pLavkNdemVUrbQ8/Snh5tcu3L7eIopSSo/G33neM4xPFfa9jxIhb4cCax3aUUjFZNfgvAb8OPHooqxpxy+i65PFjNf5WqvjMs0uQplARPHnCYK3V4TPn9+jdfgso4De+8BrlgsWPPXyERj8L92wKt41+SH5M39IcWm7IfMWhljdYafs4puRq02Ol6+EYGkGsqBUtvERhFwQ9LyZWLvVdI8ZOTVj03JDWQBEDjgmkcG5jp89yvrHKYsPl1fU+R6t5JksW40ULXUrmKg6aECy2s2sv1PKsdX1Wux6GLjANjYWxPP0wIYxSvNClYEpsQ2e147HS8fHjFEuXHB8vcP9sGSG4rWmye2VW9fyIq40+373cIm/oOAVJexDyuRdW+On3H7xmZMSIg3BYv11tsjYjI94G2IbGD5ye5MGZEhfqPVpulHWsrThEwuRPzm0cynXqIXzrlQ0+cmqKOE0xhn2ntqeMGppkomix1PIQQmHpOu8+Nkbfj3jPccWlRp/Vjs9XX16nGyS4YUxiauQsjQ/dP0Vn4LPacem5EQESkQpSJUgZVpULaO9RX6iAb13scmSiwImJPK+s91ntBYzlM+HbNrK1OqbO3FgOXZfYukAzNGaKCscyODlZBKUoODptL2C54dMcBORNiSElSQJ9P6bRzxo4emFClKRY8vCjt3tlVmlC0A9jWoOI8bIk9LL/gyBO9p2xNWLErXJQwfxdu58CZsgmCX7vsBY14s0jpWCi5FDNWVxqDkiTlMtNjSuNAe9dKLDW9Wi1E95sUu96t8dqz6U1SDA0iWlIqo6BlHLL68ibOvNVByHA0rNNu2ibzJbsrEeVpuhFKTlTo1aw6AUxzX7Ix+4vkYoyXS/ixaU20yWb1+sDBAkdN8UAEpUJbXtJOzGQJFDvhQz8mDhJEcBi0+X0ZHFrM7YNjamChR8mHK3lQAhW2h7LbZ/xosl81aHRD+j5UdbFWNNQgK1nRixnSK40B6SpwNDkgcNX+2GvzKqZssPFeg8pM/Hf1CS+grG8hiXkVgX7iBG3g4PemjzDzg67m3wD+KlDWdGIQ0XXJeMFi+8vdVAIJso2TxwfY7Hpcn6lw4X1Hi+vB/vuQ7Wb9X7IHzyzyJFanoVaHoXOcpjwroXq1sYlpWCm4rDa8fGiZEdDw4WxPOdWuphC4McpiYpxDMHUdIGpikNhmAb8iUdmWWr79KOQlabHYsfluxfarHSuP8vEkTA/ZrHRD0jjiDS1ODZpU+8FHBnLMVm0WO8FBHFMqhQTRQt7mK11ZCxHaxBkg6TyFifGCvhRgmVKgiAh7xhUHYOSY9LxE4qOyUIthwQWWy4LlRxq+C056JCp67G7SWaiFEdqBZ5+YJqvv97ATxJsXfLAXJW6G9LwolG7khG3jYMaj+O7HqdAXSnl7/XDI956tvowjWWbma1Jmm7EA/MVwlixUCty31SXz73QYO9J4zcmihVfemWD2TGXE7UBn3pigbypYwxTRjdTS6/X+iNrsZJjfixPzw9ASpI4pZo3OT6WJ29pJKQUDYPZYzlipdjoBhi65KNn+jT6Hl035C8vNPizl1tb6yoZ8IlHZ/BjwaV6lwv1ATlb55sXGzx2pJIVEpo6k0ULQ5cIBYttjzhJiVPFasfDDRM0IZirSixL46HZMs9dhchOaHsxs1WH4+MFFDBesAmTrAixNQi5sN7fen9TZZujtfyhbOI7mmSm4Jg6jy9UeXCqRDeKaPciTk4UMPVRu5IRt5eDGo+jwF8OhfIthllX79/PMKgRd5ZNoXWz/uHoRIFopccgSJBSoAnFa3WfUg7qB6wsnHIE2rCgrdXr892+x0zB5OOPzKMJsa+mfYlS5G2TD5+d5Juvb9DxY4Su88iRKs8utfncC6t4QYSUgqdOTvD0/ZPkLQNDl5ydKRPEBZSCv/6e43zt4grfudCimNd5Yn6c+iCm3ne52vQoODol26IfhHzncosfe88cQigub/RZGMvjWDrTZZvltsdSy8PQBMdreVa7Pus9HykFrWEzxFrBJIpSJso2J2oFljreVnEiStHxQzpuiCaz4kJEZjyP1vKHuolvD2UJXVLUTGxdx9RH7UpG3H4Oajy+SKZx7J4rXh4eG/nHbzN2C61SgGlqLBSzIrXnrrboeiHeAZoploHJMYmfSqIYgijiSj/rp7XavsDUmMPxicK+mvZpQoCCiaLNJx6f40LDJW9I+kHMF15aJYljCo6B68d87bV1bE3w3tO1rOFimikd85UcjqXz9Ol5Tk+MYQjBStdHkuBoGoYuqTgWEqg6JoMgZmljwGov5PmrDSQa7z87xkdPzzFZtLKmh5aBEjBdsrna8ijoAj9KmS7bGLrGTCVHkiqCNCWMU1Y6Huu9gMmSRZoqbMNAk2DokrYbM1lMbssmvj2Utd17GrUrGXG7Oajx2FW3u0WNrPh3xNuM3UJrmirGiyalvMWZ6SKvrneJkp0jW69H2YCCAZqE7iBFyBQ3iWkNDY9jQJrA//Xl13niyBipkDtSS/0wwo8TbF3boYfMVhxWuz5xkpIzNMq2xsYgG7Fr6DppCjlLp+/H1PsBLy52GcubdP0ElOL5pQ73z5QoOybjhUzj8KIEqcFM1SFv6/hxTN4wcKOIME752qt1vnh+nQutrP7ld59Z5kcfq/P3PnwfKx2fVbxsVohSjOVMjozlyZlZ0aBpaqSJQilFvRdQsHVOmgUQCkW2cYdxjFISiSA54LCoW/k/3jRKm97TIIjRNcnsAXpujRhxEPZlPIQQfzT8pwJ+RwixPTyuAQ8Bf3nIaxtxSFzv7rTsmBwbz/PkiQqfe75+0/N0ouzPdjbvJkygZOk4pobrR7yw3GZhvEB34FPMmaQqm7ehBOhS7ghh5SydB2ZKfH+xg2NI1gcRZVvH0ARRCkmSEKYQq5ScnaWirnUDbENDH4bHLm4MeHhGo95NmChaJEmatT73Yz583zh/+OwyaZJimzr3Hy3w7dcywyEBSwcvhs88u8Yn3j1DFEteXc+ymAZehK4JHpgtZ2tIUgZBQi1vM1+10YYt1uu9AF1KltoeeV0jGrYs6Xoh1bzFfPXO6Q5i198jRtwO9ut5bE4TFECLbH7PJiHwVeD/PsR1jThkdt+drnZ8wjihlnP4yNlpnl9scqF58FbuO9xQAX4YEyvFM5ca/Pm5dTYGIXlT4/6ZCk/fP0lx2BNqewgrTRVdP+b4ZD4LsaUpfT/GMXX+4DtXWez7aEJydjobH5vEKV6c3c0rMmO02vGxNMml5oDjYzksQ6dWNLF1yYmJEp/+KyaW0JibcLja8PjK+WzioqmDUlmH4Aj45isNTMtiaaPDhY0+F5Y9emk2i2S8COW8zrHxCgsTBSRVNClxLI2cqVOwDBaqAkMXDLyEUGUG7NEjFXLW7a+52KxCN3VJztJHgvmI28q+fqOVUp8GEEJcAn511H797mbTE4mGsXEhcnz8sWl+/c+XbvmcIdAexFgGnBjP87XXNghiRRJDpBJeXetimYKPPTBDwTJ2CLlbov5QfNc1SZLC0/dP89SJGqtdl76b0AljdCmo90MW2wNMTVDOmbTciIqjsdL1yBk6Sgp0TbHc8ZgvO5wcL1AfmKg0pe8rPC9hvmzz8kaEH2dfgs3Jhy035Nnz67xU35l7FgG9HtCLeXZ1g8l8iw/dF3BmtshcJY+uZR7IdMVhselxZDy3VdPSdqMsA+0mKbtvdpzsaL7HiDvJQXtb/ePbtZARd5bNvkqzw/qLp0/P882XO3x36dpZfxpZWOo6raawAceGyaLJeNGhljN45qpPFCkKlkEaKzYGMV98aZWiZfDksRq6rm1pANebS2FokmrBplqwSVNFz494brHNRMEiSRXNQUDLC9FkVjC32PaZqphcabj0vIiuF3F8IscHTk1ScnReXunR9SNSpXj6kSNcab3Gq81wq8blaMXku1c7nK/fPGl5fZDw5ZdX8KKIoqNzaiJPzjaI40zEd4ysUaI7nBESxgm2oV+37uIwxsmO5nuMuJPc1HgMJwZ+SCnVEkK8wN6COQBKqUcOc3Ejbj+bXshM2eZffOpxXlpp8O+/ewHPC8gX8pyZqpAzdV5dc/nq+fUdUwA38QHpg8orTo4XkGmKUBqCmDhNSRWYmkDTBGtdn8W2x5PHaztE85vNpZBSkLN05soOlpl1pI3jlI4fYWgCU5MEiWKt47PccrPpfrqk3gv5zuUGJyaKTJcsxosmcaqodwM+/cFT/N43LrA2CNA1wVzF5tzq/nt/rbvwF+c3uLA+oP3ukDNTFWoFk7G8QZymxLHi+attUgV5K/M8ltsecxVnhxcSxymLLRdLkzjmrYebRvM9RtxJ9uN5/D5s1Y/9PjcwHiPuTja9kCO1PIaucWqqSr0XkDMl51abeG7CbFEwWbFJmv6evaRc4JVGRCXf5cGZElMlm6VGn36QoAHjRZuFsQLzldxWYd52dldP77XhaUKgaVkARh8K0iXH3KoUn6/aXNroMwgSxosWJ8byBHHCStun5BiMF2ymyw4qVVzc6PPiUpejM2XGg5Tldp+r3YDggPPfOzEEdY8/+NYiP/0DFisdj+PjBeq9rJ3JIIw4NZHNUV/t+qSxIk5STF3baqm+2HQzod3Ssyp3Q7vlcNN+PscRIw6DmxqP7aEqpdQv39bVjHhL2b7xTBVC/slnn+Mzz29s9Y0qAOWCxIhSrhfZWdzo86n3LDBRdji/YvHqWo80STkzneeRuTGmyjaOqe8ZStlRPb0H17uztg2NBUNjpmxjScmXX6szns9mWnS9EAW0ByE5M+uKa+uCkm3gGJJqLserq11qORtUgGdB73rxueuQKnil4fN7373EfCXHctOj6Ohcbbo4lkbPS3hgrshGL2SiZFG0DVKlWGl7KMAyJAVbR6VZ6u9U0doz3LRfTeRmn+OIEYfBQRsj/jnwSaVUe9fzJeDfK6U+epiLG3HnkVKQxorf/sZr/OHzO7vv9oF+PyV/g2F1QsJEyeTsTJkPnBqn5YbU+wG54djVqbJ9oHnfu7nenfWm93RyqsRyN+BSo0/HjSg7OpWcxVzF5lLD5UrTI01TpssmcaJINYWpZ40OJYKxokPb8w7UqmXTWfnW5QHfvTxgLFfn5ESRqbKDhsklt0u943FsosCj85VM15CCKMnMct56oz6l78cEOeOa1N7D0ERGjDhMDpo/+GEy7XQ3NvDBN72aEW8L2m7Anzx3+brHbzRyXNckttRoDSKmyxbjRYeTEwovTjhSyWVddN9kKOVGd9Y5S+eDpyeYq9gstj2Kto5SgrypsdT2ma9Y6LpO0dKJE0UQK8YLJk034AcfmuFKo8/JiQJff61O4xaafcVkWsj65R4Vs8dE0UbTBGemCtw3W2St66NpMhuAZRuYhkYYJ+hSMF4wKdsGx8byO8bJ7jUIapSCO+KtZr9FgttbsT8ihGhue6wBHwduPc9zxNsKP0kIb7JxmmTpq9sFsKoFP/TwLBuDiI4X03SzTKj7JgpEaUoviNBEVqF9O8lZOmeny9imjiEFG4OQrheAyuZ0KAUFW6eSt/nImQn8KOHV9R6alKSAF8R4R1OWG02WGopbHZ/VDmFGpJiaycUNl7PTHjNlh1Qp/Dghb2pMOjYvrXRJUoUmBQ/Nla+ZQz5KwR3xdmS/nsdmK3YF/Okexz3g5w5rUSPeWsYsi1OzBS6+cm3aLmR3C2dmbGzToD3wiP2Y+fEiH3pwmiePjfPSSo+CpVO0Dbww5o+fX8aLEko5g4mCzYfPTjJddm7re9B1yXw1x2rHJ2do9D2JY2oMgpi5Sg4vzOaPVB0LvShxw5SNvs9k0eL5tku9G2DbBR44pjHwI8I4oTXwWT+gHtLyY2YdmziJudjwuG825cJqj1DB+ZUu7zo6xtGxHEIKVKpoDkIcQ9uRjXUvpeC+2VqWEW8f9ms8jpNVl18AngS297IIgXWl1MHLk0e8LcnlDP7eR+7nWxe+TWdXZpUFnJ7OMzeWx9IEOV2jOmfx5Mlx7pso0h5k/Utmxxyag5D1vs+FjT6PLowxljdpuQF/+uIKn3x0nkJ+rwjo4bFdHzk9WeTkZIGvX2jQGAQYmuSpkzWEllW4SyEwdY2JkkXLjSjmNEwpCaKYIIpwLI0jdp6kPtgRzjKAnAmdbVlaGtmXJQbCKKXd7ZNIg9WOy7/7+hVmqg4TJZMwgm9dbDD/WA5Dk/hpwlLL25GNZQ/DfPdCCu5It7m32G+F+WYA/AZS6Yh7iUcXxvnHn3yIqsjhswAAIABJREFU//ebl2i5PipVnJwuIZQkZ5uYmkbHC0mF5Nh4numSTUo24KWWNxEKJgsWjZ5P0Tao5g16fsy5lR5RFKNSxccfnr3tHsh2fWR+LM+PFmy8JEEoaLoRV5suaapIUSzU8rR6PicmClxp9GkNQhbbHmEU0+xD2dFp7QrnHasZOJaJRkKiUl5ZCUl5Q0RvBtAMUspWwFTFptnvYxsCTRecqBW43BjQDUMqmsXVposu2crG2q5r3O0puCPd5t7jwA13hrM7ngQW2CWeK6V+65DWNeItRhOCxxbGeWCqjBsn2JqG1CW6Bl95ZYPLjQFThsO7jzmcmixkvaTyJqJgUc4ZvLTcJUpSpJTMVh0GQcJzV1s0Bj45XWe9F/AfX1zhv3ji6JYGcidCGqapoaeSK013ayML44TVho8hBC0vYmMQ4iUJ9V5AYxDjxlm8diO4tsCl7Ua8+1gNXUqEEKSqwfdXr52N1gng+YsdEqAxCJhu51GJopIziaOUV7o96r2A+YpDOByNG8Rx9hkqsfWZ3K0ax0i3ufc4aKruWeAzvBHGSobniMgKCUfG4x7hjVAJ6Ka+I8wwVXB4db1H2TFACJpuSN+PqeZhfji/+33Ha4RpSpoozq11+PLLdS43BkyXLM7OlHFMjdfrfS41usyU8mi6ZL0X3JGQxuZGBgIvjDPtI2ew3PGRAk5O5Li81mWtlz22JXjXyTATCczW8uR0bauH1fXwAUdAZ6BA9YmTmB9/6hiObbDgGNiGRBdiq9YjSlKW2pnIsp/P5O2sJ9xLus2IjIN6Hv8H8B3gMWB1+HcZ+HXgfzjcpY14q7leqMSxdCp5a3jnng2YypsaC5Uc5nBz03WJjiRNFY/MVZkuWXTdiKmiRc4xWNpweX6xQxQl5GyDszNlHjtSobBH192DkqYKL4i3Zoc4ln7NAKp+EPHc1T4qVQgpOFrLWrQgYKVjUSta6I0+jiXxI4V3Hevh2BqnqjnqXowmI+r9G09kNnVwI2h7MIh9nrnYZLacp+SYJCk0vABbk5RsHRRYutzabG/0mbzd9YR7RbcZ8QYHNR7vIetzNRBCpICulPquEOIfAP8KGPW2usfYK1SyfSPo+gGNfkgtb7Lc9XdsWts3NJXCo/NVXqt3Wer4vLjUppQzmavliRN45lKDmYJFbkp/UyENP0p4YbHFN15v0nZDio7OU6dqPDo/trWuNFWstDzabrTVEt7UfZyaRsuLmKs6zFRscqZBz9+dkPwGBeDB+RKphLJjcHG1zeXmjadqDaJMULf17LQvL7X4M1Pnhx+epWCZGBIGUcJk0WJjEG61Ydn9mWz3MoDbpiccpjdzt+s2I3ZyK5MENydd14E54GVgETh1iOsa8TbHNjTmKw6XGgOO1nKY+s5NC67d0E5N5wnShHI/oNG3mB/L48eKiqOj2tCJss0xTa6dvLefTSxNFVeaA5670iJKUkwjGwH75XPr5E2dB2YqW51uG25mJHQp8aKYKw2X8bzJcjszgE+dnsI2DP78/CrNfsyYGaEbAh1FqLLMEaXgxRWXi43X+PCZSXKWiUYWy92LnAR36MC4Q0X9Uj0iSlYZKxgcrZUQSpC3NFa7Phv9EAnkh97Y5mey28uoFczboifcDm/mbtZtRuzkoMbj+8CjZCm73wJ+UQiRAH8XeO1mLxZC/A7wNJAnC3v9S6XUbw6P5YBfBf4GWQbkc0qpHzjg+kbcQZTINgNTv3bTAq7Z0BxD5/G5bAPvBPFwSmBK308o2jq2kARhgqbJHSGN/W5iiVL4QSYyR0mKZWroUhLFCUtNl9OTJVQCiy2Xes8nihMmyzatQYQuBZNFm0RlRuHsTIlTEwXef2qMbhBTMDX6fkI/THhxscnXXtnAMCS6NKi7Ln/xygY//t5ZTA3CPazHE9MGlmNzfrlHK4Dt/smrzZT6Vy7zn79njlNTZTQpKFpG1oW34zPNG9MX4VqjXO8FWWrwIeoJd2t21NtZ97nXOGjq7T/jjemW/yNwBPgi8IPAz+/j9b8CHFNKlYBPAP9UCPHu4bHfAMaA+4d//zcHXNuIO8x2ERTYsWntPhZG2Y5qmhoFx+DpB6YRKNY6Hj0/5ENnpnjvqXEWxvMsjOV2hJg2N7GsrbnIDEl6bShJEwLbysR9P0pJU0UQp1i6xDA00lRxuTFgvRcghOL8So9vX2jQckNOTxUxdY2pkk2cKgZ+jB+nVPMOedNC1wzOzlR4aLZMKWeBlNRKOYp5jYJl40UJQST4wYemdvT+0oAnjhR54vQsjmXywGyRvdSTdgxfeXmVC/UelZyBElCwDWZKNrMVZ+szeSNr6Y1wFsB40SJKFIMgJkrUm9YT9rpOqtTWjcHbET9KuNJ0udp0udJ08aNR6dnt5KDDoP7Dtn+/DjwghBgDWkrd/LdKKfXi9ofDPyeFEH0yYzKvlNrsBvGdg6xtxJ3nZiLoXrqIUtBPY8byFn/98SM4jkbVMsk7xp6b3X5TPDfvOOcrOR47WuU/vLDGWtunkjc4M1tmrvJGKK3rRZRtC1XKpvyVLEmtYHK15RKrFE0IJopZsaBlZSNdVzseV1se0yWLo+N5dE3gBjG2qQEpKEXXizg7W2WmbOP5Pq9uDDgxUaQXQHMQEceKQs6kIKC/69uiAWkqUUiSNN3yJDRNYutv9AO7XtZS3tTJj+mHdtd9t2VH3a2e0t3MfoZB/dF+TiSEQCn1iX383K8BfwdwgO8BnwM+CVwG/rEQ4m8DK8AvK6V+/zrn+BngZwAWFhb2s7wRt4kbiaDX00WCOL1mINL12M8mtjus9fBclVPjRa62XHRNkrd0ZipOlluepjS9iKKtk7NyFJyIkmPy/eUOltQwTcnJWp7GIBMldC2rbVmo5el5EQu1PLWCxSurPb50vk5nEFGwdd59usxkxaFkZ97Fi0sdpmsVJgoGLyx2GQQRUxWbNEkp56C/a5CzAoRMMfVsI7xQHzBZzAzV7qFYNzLYh6Un3G3ZUaM6kjvPfjyPxmFeUCn194UQPwc8RdalNwDmgYfIhk3NDo/9sRDiJaXUuT3O8RtkYS6eeOKJt68f/Q7hRiLo3rpImr1mHxvRzTaxve4413sBC2M5qgVrh1FLU8VE0eLCep9ACoQQ1PI2bhQzXbYYy9mkSuHGKSUtK/rbNFpZRlbWc2qsYPFff+QMHz0zQcuPKVk6/SDF0CUdL8KPEtwooWDrfPmVDdqDiFSlfOB0jeYg5uRUBX+5TctnK4RVseHsTJWPnplmbjjESikwh9fe/j7uVNbS3ZQddbd5SvcC+xkG9enDvuiwD9ZXhRA/CfwsWWPFCPinSqkY+LIQYlNLucZ4jLh7uNUv9fYNc/smJlRmkNI0m4lxoztOQ5M7jJqUgpMTRda6AS03wNA0yrZO0dQpWAYApq7R8yKELZitOKz3gj2NVsExeOL4JFGSsth0ESLANjRKlkHXD9ElXFnrUysYoFL8SFHv+rzv5ARTJZPTkzk6bkAnCMmZBrO1Io/PV0FmGoNj6gyCmEEY0+iH1yQL3KmspbslO+pu85TuBQ7cnuQ2XP8ksK/Q2Ii7j1v5Ul8vuyqM0mueNzW5ZZykFFvC/PWMU87Sef/JcRbbLipR6LokThS6Jmi5EW4YkiqYrTjkLJ2FoUi91523lAKpBEIKZioO9WGFvCLrW/Xs5Rb1fkjLCzE1nbVOQCVvoWsa1WIOJQxmxiTlnMlTJ2v4sSKIkq10ZQHUe8GwUDDzqnbPQE9TtTVUavtzB/UWdr/mbsxaups8pXuBO2Y8hBCTwEeBz5J5Gh8DfgL4m8BXgCvAfyeE+BXgvWQhrf/2Tq1vxO3jIF/qzTCUJjIvQg0fz1ec6wqi02WbyxtZFhXAZMnK+kPJvWsScpbOqYni1nrC4bmqjoESBrPlzHDAze+8Nz0rXQrmKg5BnBDZBheSmCCM8cMER0KYxDT6MRfqfT58/zRdN0YTgqJj8MTRKtWCzcCPWA6TrXTl8aJFvRdsZTzFqdrRdbdk66x0PJq9EKRgsmQxVbJpu9F105r3Mgq7jXUlZ9zwHG9n7hZP6V7gTnoeiixE9W/IUoQvA7+glPpDACHEjwK/CfzD4bH/Uil1/g6ub8RtZL9f6kQpvChmECRbm1fe0rI+WUohpcQNI8IoIUoVUZJ5H6YuOTLmYOnaNR1pb7YeW+7PuO218W73rFKVheSmqw4tN6CQs7jc9IlU5kXomka9l80GGS9azFcdxvImupbNGZFS8th8BakJTJl5EQ0RbnlVqx0PQxMUbYOBH/GF8w1SlWJKjWo+a/m+2vI4Mp7DMfRrPoe9PDpTkzuMsh/FPH+lzVzZRkloeQEX1zucnioyWczd9kFeI+4e7pjxUErVgQ/d4PiLZEL5iHcwQkGjH2Lrkpyp4w9j/idrBaI45eXlLs8uNekMEhxL4+MPxNw3UyJNFQUn0y0k4sCZNjczbjcqVNztWcVxSqIS8qbA1BUVy0TTJH0/wAtCqjmTsZxFEKcUbYMj1RxKQBSn1zSH3DRMYRATxoqFWpZyvDEIGfgxqz2fnhcyCBJmyw6GLlFSUbBMJorWG7UZ6d4tTGbK9pZmtPkeX6/3eHapyetrfZ6/2iZIU2p5k4/eP81PPHn0trfRH3F38FZrHiPewewVr1cimwfiRgluGKNJkdWHCPDjmGevNvHjhJJjoAv40it1BmGMqevMDgvrdovybzZ+v58agk3j03ZD/vSlFb7+6gav1we03IgYjZIpmKsUmChZhFFKP0iYLFnMVBx0PcuoWuoFe15jYSxHlKQYmkQfJgkEScx6z2fgRQz8lCBJeXmty4nxPD0/oWzDSttjqmSjiesnFkDWsTeME9a7mS5V7/nUez7fu9Jk4Edomk4QRHzjtQ0miyafetfR63ogd4NWcjes8W5gZDxGvCX4UXKNTnG0lsfUJI6pU7CzSvFUKZI0+/mVjo8fJnSDCNNI8IOYsYIJAmp5neW2z2yZHe1NDqM/035rCOI45euv1/mLV+pYmmSu6tD1AkwSHj9SYXYsT962eP/J8Wy++rY6lyhJCeMEyzCuuYahSSypMVNxWGl7BHFCHCkmCiZ9PyJKUwwp0E2dUs4kihP8KCWOU8q57HzXy3ozhp/V6+s9Lm4MEAKEgI4X44cRuqajGZIYiRvFdIezTrIuXtf+n76dO/vC3bHGu4WR8Rhxx0lTxXLbo+2FFO3sV7A1CDE1ydFanvG8yWLLzWL/msZk0WKt6yOF4krL5dXVHv0wRgjFXDnHkVqevJVN36sWTCqOuZUxdBhVx/tNN277IS8ud3H9mFDXieIUxzDxopjlTkSloHj8aJGmG1FKFI2BS7MfMFF2SBPJUsfDHgTMVnLoUux5DUXmKUyWbVY6Ho6uM13WyJs6PT/EGbZYqTo664OQ5iCk68U7QmC7s95MJJahZVlcuqDrhqA6SKmRJilaKklFTBiDY+s42rWb7e2u8D4Mb2FUhX64jIzHiDtOohRxkiKE2MokksNmho1BwMurPcIoIUFx/2SRIE4Io4RUQcsNCOMIBaQKVvo+r671eXiuiqVLOm5ExTG3rnMYVcf7STeO45SVposmFZqErh8wCBJMXTBXLTBXyTFbdaiYBt+6UOf/+94Sz1/u4GdyBPePwccfnGSyOk7fjzk5WWS24lxTDGnpWcV8nKQ8OFcGBS0vwo8TFmp5bEOn7JhsuBHzFWerI+/2ENjuTTgZ3oXPj+XY6AccHS9wYaPPgpew1Hbx4pi8NDgzXeIjZ6b3DFndzgrvw/IWRlXoh8vIeIy442hDo6GGRgSyzVEIeHmlh5CKQZjwWr3L559f5r7pApamYVkaOcuk4EQUEJgS2l7CcsOj50ecGC8SbnbUldqhVh3fKN3YjxIurPd4cbWHITXGciYrnR4DP2K66vDY0TGCWNEeBPzmX77GV8+vsuLuPP+5Jpz7i3Vq5jo//tRxHpuv7Ngg99r4yjmLv/bYHCttD6UUhqExW3KQmmC57ZG39w6B7d4oNz8nKWCyaDGWM5goLnC14fL/t/fmUZJlZ2Hn77499ojcK7Mqa69eqrvVKnVLaklIjRYsDOODOAwYo2HEJgxmGzyMZ5jxGGMfe6RzfJgxw2J7jDDLwRgxDAaEQCto61Yv6m71XntW7kvsES/eeuePF5mVmZWZlXtmdd3fOXEq470X790vsvJ+937rTMMlDGMG8g5nBvMc68ms+zvdiwzv3dwtqCz03UUpD8W+o2lJ9vZidBEkPo/erM1ExaXRDnh9ps6rUw1mqi5TVZdSxiRvCQgDKi0fG2hq4JhgmIKetMV0vUMQJZVgh7vtcHcz63itiKw4lkxVXapuQCFlkrUNUpZOPmMwW/O550gOxzKotdq8cKPO2Fz7FsWxnAUf/uBLVxnM2Xznw0fpydrA+hNfIWVRSFm3JPgZmrbpSVLTktyOFydqRLFE1wQPjBQ4O5BnvNImlhLbSHwuG4U+70WG927uFlQW+u6ilIfiQHBMnbODOU70JStZs1vDCWCi2qHuhsw1Pcptn5mmj1MX5BwDGcRUvJv3sV040Z/0+i6kk9wJy9CWVqd7nXUcSYkXJlntfTmbajuglLE5UkiTO6czXu5QaXn05W1em4GFhn/be5ZjmGm0+cZYmfecG8QwtE0XRFz0DQzk7HVLq6wmjpPqwsd70ghNILvvR3vSnBnIbfq724vverd3C+uNUUVgbR2lPBQHhqYJ7GVZ4JomuO9InhcnqjTcgHLTJ5ISx9SxkEyW27irWoR7wEuTVa6Xe3k4a7PQ8lfkN2iILWUdb3USCcKY+YbPXKODZWoUHBMnm4TgnujJ0B4JGSu30IGXJ2rrdbS9hafGKrR8yan+LCf7c8TdHcHRYgopWHN8q30DAzkb09BuK8vi6j5lJdNBLCRuJ0jMf6a+Zf/QbvoP9mK3sHqMKgJreyjloThU9Occ3nPPAJaucXG2wUwtINIMIl0gZVLXZjU1FyYWWjxyvA9DE0xU2vRlbcQW6y1vdRKJY8lsw2Mob9PwfF6dbqALwfnhAn1ZG8PQyGomWcdCCMm9wwW+9uos9dtsPhzA60QsND3+5vU5ejM25VXlQkxTW5EnowuxbnXh22XMC8nS6j7smuFWm/8Okr3cPaoIrO2jlIfiUKFpgrODeWIpuTzXoOMF2FZS0r0TCCrerRohBFrtDs0gwIqSySDnGIxX3aUSHJspPbLVSWSxlMq1+SZffGWGQELe0claPUmTKcdcWjlPVV2Ol7K8+4FBPv3NGZrB+t/B6SNpUo7NaE+GsYU2V+Yb9OdSK8Y1kLO5UW4zWUvyPkppg4xl0p9Psr9v5xtoeyHjlTZhHGNoSR2tWjtgvOJiGYLR3iRc+LBMpHtVs0pFYG0fpTwUhw7H1HlwpMSPv9vgDxyDsbIHMqI36+BMV7lSDW/5zGdertJbnOWtp/sYKtj0ZZOyG9fnW1iGtpQfsVH/861OIkLCTK3D1y7OM1X3MDTBXDXmS9Ys3/HgyNJnHVNPAgSimNNDZznRl+Pzz1/nuZlgqZ+HBgynIZMyGC6k6cvaZC0DL/AR8ma7WU0TdDoBY5WQiYrL6zMNJqouQRQzmHf4tvOD9OdSS74BIZMExMVS9rpIEie/dnme2YZLsxOScUwGcw4XRksEUUwuZaJ1fQpv9IlURWBtH6U8FIcSTROc6M/xvY+ewPVCpAazDY/LswU+8bnLzKxaubvAnz47DlLyrnuH8KMYy9CYbXgc60mRtowNdxPbmUSkAEuDsUobyxSYmg4Inpuocd+RAsf7s5jdSd/UtW4zKcF3PDjC+aE8VxaaXF9o02h5pGyDUtZhttZhoeWTtXWaQcRDx4pkUtaSSWm65tL2I8Iw4rWZJtV2QE/Gwgti2p2A58ervPW4jmnoFNMm41UXNwiX2gDbpk6l5XFptknLD2l7EelOSN0N6c9ZpG0TP4iwuj3fIclhiWO5qc6PdxoqAmv7KOWhOLRomuBYb4bpWocoimmYEY+d6mds1uWPn53EI1mx6wJCCW4A18pN5p68SH8+y7vP94O0sI3b7ya2M4noQuDYOmnLIJ8yqLoBQoApNAbyVuJzWGzctOz+Ejjen+ORU30QJ+a5QsbC0DQqTY8XxqvkMxYFy+RNx0s4ps5k1WWi4qJrcLSUYqzcotz2EYCuCUxDwzI1etIWAwWHjJmY7XQBLS/CMTTaQYSlC16faYKAjh+TtpOCiLmU6PYKEUy5yc6ukEoq844ttNCEYLDgcLw3sy0fyEaBCAcd6aT6gGwPpTwUh5rlf9iLtZ2O9TtkHPC6bVy7C2RaEXzutVr3k3U+8cQkH37bMEdLZ9YsmLjRszYziWia4Ex/npP9WW6U28RRTNY2Od6fYaiQIYjlCkW11v3jWFLMOF1fi0ZvzuHtZ/oZyjs4ho5hJDuXkWKKlhfSCSIq7QBT18hYGvP1RGHlHJO0qZG2TDKmQSQlfhjhmEmJ+rRl0PZDhJYkAxZSOtPVCBFAEEkKaZ2mG5EZMujLObhByOXZJk0vxDL0RIK6u1RCZjMT7KJDvxNELLSSbohBEFHMWpRsC93U1qwkfBAO+o18Kget3A4rSnkoDj2Lf9imrnGyP8v3PHKSWivgt798g84Gn4uBP3pykvOjBS6M9GEa+m13E1t1zObTFj/wtuN8/tVppmsdMo7Ju872dbsM3trRcPX919rxHC2lb5lAdSGouQGOkRSO1IDzR4p4AxHXFloYmmC0J8uDIwXcMGKm3mGm7iE0iR/EtAMfUyRKoD/noCGptELqrs9AzqY3bQMaKdNA0wSmpjHf9MnYOlnbIIol1XbIQC5aUogbTaqLhS+TcXTIOwYtP+Qb16u0PY++nMO7zg4gNI3hgrOijMpG4cj7zX6H8d5JikopD8UdhaYJ+nMOH33POYIootwKMHXJV1+bZ3yNzG0XeG2szkPDvYzk7D35wx8upfnetxyn0vGptwN0XSOI5KZt54YQ9HSr36YtY2m3sZy1StUf78twojezFK6rCcFc01uKmCqmDV4cr/HSVB0/iBgspHlgJOJNx4q0/YhSxmKh5dOXsUg7JlGcBA1oCII4RtcEuiYI4xiBIIrl0s5to0l1eeFLoUmqrsfzN8o8c61MywtpeBFpW/DadJ2/d+EYKUsnZRsYuka943FtoZUo2QPOudjvMN5OEDFZdXE7AZGQnChlyWesXX/ObqGUh+KOJGUZnBooUWh1AEEhVWW8fWsUFsBEpYLFqRU+iK1yuxWhZekMWin6s86WVo7Vts83xirMNTw0ITg3mOXcUH7NncdapeptM5l441gyVm4jBNimhibg4kyDuheQt02ODGaJpaDpB7S8kOM9GWRPekUU1mI7Xi8MEQjuHcpRcwMq7YA4jimmLI6WkmZUG02qi4UvvSDmWrlNre3z2lSdStMHDRxLJ4okV+fbfOW1OfIPGgzlHcI4ZqHpc7w3jWUcfM7FfobxxnESGXh5rsFLk3WCMCafNvjuC8cYLqZ39Vm7hVIeijsS29R56FiBlyc1FpouR/qyvLRQXfPaXCpHKCAKI7wgwliWdb0ZM8FWTBdbMXuFYcyL4zWanZC+rE0Yx1ydb5G2DU71ZVeMZ7l5K1hsd7tsZ7OUJW7qhGFMue0xttBmqt7BMQT1jo4ORHHIXN1luJDCNnUibsq+vB2vkCyZv/pz9pI5LW0bBFG84aS6eL+5poeMIW9bBMTJfWOwdUEoJYYOpi6YrncY7U9Mdr0ZC2sTAQ77wX6G8QZRzGSlzavTDXK2iZXVqLR8/vb1Ob774aOHsv2vUh6KO5LFZMK0aeAGee4ZzHJp+gWu1eIV19nAYN5mtu7ih5Lp7mSYMg2KaZPqqszt1UphJ6aL2ykmP47xoigpIaIJdE2n7Sfl59eaMDdy6C+f6BDJZNT2AwI/YGrB48lLU8xVItIOPDDaw3vPu9w7lCdjmytk1zSBH8RLylIXgsFSmoxlLD1PyEQ2P4yWdgiLk+qizP1Zm5ShcaXhIeOIoZxDqx1Sdf3EzCagkDIppCzuPZJnuJDCMXTGq+6hybnY7zBeP44II0kqqxPFEsfUCKJ43eZbB41SHoo7FsfUOdmfJYhiqh2fYtqC2koX+kPHMpwcLGDoGroOIpZMV9sM5R0uz9Tpz1kU0jaGvnY29XZNF7fbrcRxsro3NY22F2DpWte3AJapb1gBd63nLk5045U29Y5Pwwtpdny+fn2B8Vq0dN1CC9wrZTQ0ZAyPne4DAePlNqPdXuqTVRfb0JaU5ULTJ9NjrJAriGOmFzr0ZixSlsFQwVkye8VSImOJYxo8eqJAy5cMFByE0Kg0XGYaHr0Zmzef6OXBY0XcICnzMlxMHbqci/0K4zV1jaOFNM/dqFFr+1iGjq1rOKa+ZvOtw4BSHoo7Gk0ThF7MHz9xleemViqOFPC+80copE2GCylenq7z8mSdqWqbKIyYb/mc7MuRT5k8fl8/PWnnFqWwHdPF7XYryxVLKWPiBhHzzZs+j6Ol29ejWmsis3SNlKFxcabFfL3FE5fmVyiOReY9ePLyPEM9DqWMiaXrtPyA8Uqb3qxFuRVwtJQirYkVypL4pq8jZSUZ8A3XxzLA80IW3GBJ6bh+iESScSwcU9KbtXj4WJH+rE3LC6i5IXU/ImsZDBVSK0qhHLaci70qjbL6GaeH8nxrGPHU1TJBFFHMOXzL2f5DabICpTwUbwDm2y5ffHn+luMuMFetcmYgT8cPeWm8xmy9w0zN5ep8k04QYYokEfEvX5jme95ydM3Q2q2uhjfarSyfgA1dxzY00qZBf85OepWv4dBfVBgb5UR0gojxSpunri4Qx5KWH9Py1w4gAJj34fnrNYJAYhgajmlyqi+DjCUVN6DS8hjpSdObttC0xEcUSUkUxRh6kn0+0+jwqW9OJRV/BQyXbE71ZRkqpnB0gyCOyVoaWafb2TGGI8VEMXqtZ+2hAAAgAElEQVRBxPWF1pqlUN6opUFuZ8Z0TJ1HjvdyfqiAL2MyhnFoFQco5aF4A+B7EZoGxLeeG69F9GQtvDBGxjGT9U4y2ekGRd1gpunhOAZhBLW2jx/FONrNkNNISgwhGMzZBFGMLgSie249BbLRbiWIYvwwwjZudvkTWkzKNpZKmSxncZcSRTFT9c6aORFBFDNeaeMFIY1OSCRjXC8kZQg2qgF/da6BG4UMZlM8PFrCDUIuzvncfyRHuR3S8UMmg5gLo6Vkx+RFTNU6SGJEDF+8OEcUxwwXUjxzdYFPPjNG2gDHMrhnKIdj6fRnbB441se9Q3mO991MLlws1xLHEk0XS99REMZMHIKkwd1ms0EXmibIpEzW7td4uFDKQ3HHM5hPM9pjsjB7a6nat5zKkzFNjhVSPJOrkLPamJrBXN1D6GBqAkNq5DMGpbTFVNWlmDJYaHrcqDS5sdBiptVmZmqWV+cCcjY8ds8Q948O8eBwif5cUsV2+Ypycbdyo9yi4frESIbyKRaaSd2q2bpHueVzpJiYa5abwZavTuHmLkXTNGIZs9D2sS2ddifgWqXFxZkK1XZIre0n/TikpOhYaEInFgITyVoFfAUQATnbQmiC6+UWnTAmjCGWkv6sxXAhRRhLTCMp/36jW4W30g6ouR4zTY/TfRmuztT56pUFmgHUPKAVcrVSIaNBX95krNKhlD7B6f7s0vPX2tEtNrBaz9x3JyXQLeeNWvZdKQ/FHU8+a/OLH3oz/8N/forx2s2V9vdeGOK/eeg0rh9h2jrvu2+QlyZrLLQ8ShmHdhAQxSB0yWOne7FNg69emeOJS/N8c6LC9XKw1maGr01Ok9Oneeh4gb/38DC9WZuCbVLKpZayw6ttn799bYYXx+tM110KaQvH0nj4aA8n+jN0vJCLM3WOlzKM9KRxvZCFVof5lkfGMrFNg96sRSwlYQyz9Q6VdsBUtcMrE1U+/c1Jrs236AQRmbRByTZJpyzefLREMwgpZU3ODBQZLQa8Nt1gbln3RZ2bNcHsxUgvBFJKUpbGq9Mt7h3KMlXvUMpYSzum8bKLHyX1sUxNw+v4TJXhuevza5aYb8XguAEvTzb4y+cn6cnYnBlMcljWam61kblveQTYnbYj2Wm+yGFVmkp5KN4QPHqyn0/99Pv46rUJXp5o86ZTeR4YHFhhMhrty/IzHzjHZ1+aYb7RoeMHFLMO9x7Jc24gz2uzdT79zWnKtTpXy+v7CwAaETxzpcaL4zVKWYvejMO3nO3jvfcNca4/x599Y5yvXp5lrhEQxTFtP6I/byfZ00bMfLlNIHUW2h3Ga22evDrPN8dqpEyd+0YKvPtcH1Ecd7O1PRxDI2vrPHtlji+8NsN0LSIgMUq5XkiYjbHdkBc1jUdO9DCYtxkppShmTEavV/jmRJWLUy4SEAJSNgz3pDnTn6HmxfRmLB452YOMod4JMXUd0bV6xbHE9UNmGy5Zy6AVRMw2ffpyKTqBx1Rt/e8qiMD1feqdoLv61jnWk77Fd2OaGsSsae4TcuOkxMPOTvJFDnOXQ6U8FG8Y8lmbDz5wisfvSf7gWt6tDu7RniwffluKVhBiCEEMlFs+nTDixnyLMIopr9Fwai06gBYnVW3jOOaLF+fJpXRMQ/Lk1TJtP8INAvwgohPFEPkEfshz12aZqPsYQidtaRzJO+imhh/FdKKIr19eoO16PDBawtAE35yo0PEkugEL7Q4ztYiQm94MF3CbMWli+tJtfD9PjM0jJ3q5Nt/mSCnDPcNFpqttbsw3CGJJ1jbpy6YY6csgJDx4NE9PJgn1LWQshvIOhq5Rbfm8PlNnqt5htt7hehDTmzHRhOREXwZNTzNZbvPEjTVqwwBuCBkn+R3kHZPpegchIGMbS4ogqeabwtS1NYMTpOCObti03XyRw27uUspD8YbjdrH5lqWviGLJOSZtP+RI0Ukc4pFkHf/7LWgCTDQytkm94/PaVJOsY1Npdrgy16SyrOXsXMsHkgNpDQbzOvWOx3jZ41jJIgS8EOI4otxy+dKr04xVo6Xij44AXSZ929eiDTw95TFTv85AwWC06HDf0V7uHS5xpieP0ATXFhq4fsyJvgxDhWTCRsJ8yycIY2IJvekky9sPI+abPoYGpbTFYCHFCzcqSJm0qB3MJ+11zw6WeHm6TX0N05VO4pN6aLQHISCM46VVtBdENDs+r8zUuD5n0VtwONWT40jBAVgKIAiiJP/lsCQPboft5Isc9i6HSnko3pBsJTZf0wRpy+BUf54LJ0uMV10KYYfKerN0l4wGWUvHtHTafkgQSfpzFikH5lqdFYpjNe0YrlZvzrbXKz5pC9o+BMBM69b8jM4me7LfaMGNVsgzk03+5OUmguu8bQhOD/dwaijH++8fZaiQQRdiyd8wahtLZe9nGx4tL0zKxaeTPBRD15Yy4aWU5GyDjh9S9wJsS6M3Y+BXwxVVjrM6DORNerMWz16vMFlwOdmXoRNEzNbrXJlv8vmXZpioudiGzmDB5sJoD++9b5CMba6oABCEMX4UJw2puit3YCkCbnEyXh1wsFhqJZLJl7e8odVmfQm75XPYar7IRuauw+AHUcpDoSD5wz7Zn+WD54cppUyevF5G9wPqYcRkpcGl8s1rbeC995U4kk/x9I06fiQxNZ23nsjzlhP9VNwOTXdjn8lqAqC2gbLZCRJ4YhqemC4DZf7Fp67zwbMmRwd6ONabZbSU42ghTYzEsQwG0ilMW0dGktdn60xWmnhBxFTVZTBn4VgmxYzBjbJHKWXy6lQDPwhvKY9fSiUNpMYqLgJBytDwiylm6h1qnYCXx6tM1jo4lkbGMqg0fZ6+tsC9wznO9OZ4caLG8d40KcMgNGK8MGakmEIXAjeMmCi3l5RDb97G0jTmF/uGhEkJlDiW3fBiiaXrDORtjvcmgbCb8SUcpM9hPXPX8kz+g/SDKOWhUHRxTJ17hvKc6s/yoTcfw4tisobBnOvx6edvcGWuiaML3nX/ER4Z7cMxdcrtDpfnGtiahhvBaCmNKIccAqvChnz6YgAXZ4CZFcczwKMnsjx2Xw+Vus+Tl6f5xvTN8/f1Gdw/2sPx3gzPXp2n1mhyoyyp3LpR4kZTMt5sMJTRMQcEnTDmhfEaR0sOxYyNremkLA1T1zF1jU4Y0/YixssdUqZBx4+WEggTk02y+5hreFyea1BtBZimYGKhhW3opByDB48U6M07zDU6RN2IrmYnQAhBPm8w1+wkfVYMbUUJlrV8CYfB57Da3AUwVm4fCj+IUh4KxTI0TWBrOvayldzxtMkPvescrSDE0jRS9s0igSN2liOFzIoM8L5MinMDOZ4aa7DGnHqoaQFfvNbki9eaa55/ZT7k1fnZJFFyE/eTwFQroji1gGPoxFLgmLDQ9vEiH10T3VW1xPV9egpperImphDMdELC+KbJRgBzDQ/fD7k826DW9nllqokuwDQ0crbJQtPnLaM9SBJ/VLMTUnNDvDCk3PZJmzodL2Kw4JCxkxyd9XwJh8XnsNzcdbuKxvvJrSmte4gQ4veEEFNCiLoQ4nUhxI+ucc0/E0JIIcT793NsCsVGWJZOKWOTSZm3rPA0LelymLYNRnvS3Dtc5H/5zvM8Opo7oNHuLZLNKY7lvFKGP39+lq+8NsOL4xXm6h3AwBQQRpKGF9KfTfHWU730pGwMU+f0QIYglLS8EC+MKaRNwjhm3vVpejF+KGkHIUIDL4iRxMzUOkQyYqHpEcYxDS8kiiNcP8YUgnYQ4Vg6lXYSBQes64BfUal4g+v2k8M0pv3eefxr4EeklJ4Q4l7gi0KIb0gpnwEQQpwGvgeY2udxKRS7wuIq8U2jvfz6hx/lVz/zEr/31MyaWd6n89AOodJOfB532i5lqwQkRRm/dKnO8ZLH+aMFzg3nefNogaxjMlPzOdGbwTQ08o6BqescLaZww4j5hsdcrcOluQadICJtCeqtGCkFUgpStkYuZeH6EVEsydgGBcfED2KCwKQTRsRA1jYYLqTwwhgvigliuW7o7H6XZF+P1c7xwzAm2GflIaV8afnb7us08Ez32P8N/BPg1/dzXArFXtCTT/Fj772fAJ0/f36Satch7gB/9+EB3nv/EFEg+fLFGV6fazC54OJ1oN79vAYM5QRp26bc6FC+TfTXnUIEjFU8YjnPcCnNcN5hzggZLaUxDR0pYabuLdXUWmj6tL2QJ68t8PyNKrO1DqW0Tl/OZrQnRcP10bWkudZDI0WO9WTQhcZIIUXGaXO6D6YaHQQSw9AxDQ1N0zbVK32/SrLD2lFd6znsD0Pl4X33eQghfh34CEnF7G8An+oe/28BX0r5KXGbLZgQ4qPARwFGR0f3crgKxY4YLqX5n779PB94YJCXJsrEkc7pkQyjhXzSfz0j+PFvvYdy0+XZ6xWKWQsZx1ye61B322RshyCMGC+3ma63uDYX3BLVdCcSAjeqMeVWk3IzxNB03n6qn/c+MMBAzkaS+DGCKKba9vjS67M8dbWMH8dIGVPrSHRd49xQHj9wyDgGedvk/pE8hqYzVHCwTJ3hQorJmkshZVJpBxQtgyiGoYKzZq/41exXSOxaSsLStQ0d9ged6yGk3GTw+G4+VAgdeAx4HPgYSfTjN4Bvk1JeFUJcA35USvnZ293rkUcekU8//fQejlah2DlxLJMOetxMfmv5IfMNbylTvOkF1NpJz/COn9j5cymDiYpLytQotwMmyw0+/fwca+dz33lkDejLGhi6wWAxxY98ywlMTWe4kOZIMcVUzeVvXp/hr16a4tJUHSnB0OBYX4bjpTTvPDvI6aEstqZzpJjCMnUsTcMwtKUJOYxjkDCUd7AtfdOKYDfDdDdSQov95xMlkQQHBJHkSMFhouqSsW+u8VteyLGe9JoVmLeCEOIZKeUjO7nHgURbSSkj4MtCiA8DPwEcB35XSnn1IMajUOw1i1Fcy8k5JhnLWJpU/G6pjv5cEqI6XEhhGhptL2Su6eGHERO1ImeH8vzGX1+mvv/rvm1jkTjZF7NfTBIfiCaSWlv5lM50rc3Xr5TJp2xSlk4oJaYumCq3eO5qfUVm/eyNFlenW2Qci568zVBW58XJOoNZG6ELhnIO8y2/27gqKWE/3/IZtTcX0rqbYbq3U0LrRXXB2rW+Dktm/UGH6hokPo/3AEeFED/ZPd4P/BchxMeklB87sNEpFHvMcvODo+mc6M3cskK1TZ1C2iKSknuHCjx+boAPnh/mNz/3An/4fO0gh79pzvVrIHVmmwGNTlKPC6AegNkKiWJBxjIoOgZDpRQ1N6DaDjiStfnK5YU1S7KUA/jMixPYhmC4mKaUsZmqN/EDyDoaZwYK9GWTTPSthrTuVpjuZpTQepnk69X6Ogx1rWAflYcQYgB4L/DnJP933g98P/APgF8mWYws8hTw88Bf7tf4FIrDwEY9yhePG8DJgTw//+0XeHX6Czw/c8vlhwoNqHcEWdvAD4MlxbHIgg9eGNA75BAiCIKYKzWXTiiZybh0gnBpp7Ka6Tb80ZPjZDM6URjhhmCZGo6h8YH7Bvn+t50kn7G3vGrfSSXc5WxGCa3X2ySSEkvXDoVzfC32c+chSUxUv0ny/+k68HNSyj9dfaEQIgIqUsq1M5UUCgVZ2+TNp47y/Mz4gTzfAIoGCB2aHrcohUUkIGVE3etQXadqS0oHNEml3iFt6tiGQV/WoNrqoGk6th4QrBPLXIugVr95UvdjImJ+74kJZustvvftZxjtyW5p1b5bIbGbVULLI6gaLZ+XJ6voQpBPWYz0pA9NGfbl7JvykFLOkZinNnPtib0djUJx55OyDd5+oo+/enGSqcZW0/Z28FxgqKBRsHUymTSuH1Brh8w0fJprKAcJNH2wdYGGXDPBUDMAAXUvRKt5HCmmGCo6DBRsvvXcAJ95eYpONWAt3aOzMkcmIlmdRsDV+TZfeGWGn368sOUJeDdCYreihDRNcHW2wSe+fJWZuoup6zw8WuSx032cHy4eql0HHLzPQ6FQbBNNE7zjbD9/5/wwX319mtdv08Bqt7BMGChm+PYHh/n6lTJNLyICMikT2QroxDcncw2wBWRtga5p5PyY2ho7CMvUGMnbnB8pkE+bOIZBuelTzJhcONnDYMHmycvzjM3XuV6OWKwhaQGaDm60slt73D0nkXSCiOu1NsWcveUJeLMhsRtFU21WCVUbHv/pq9e4OFtHR8c04MnLC1iGzpn+HCn7cE3Xh2s0CoViS+TTFh99/Cwn+7K8NF3lz56evsV8lGJ9k9J20AX4ocQLItKOgZSQdQy8pkccgyHAlGDoEEWgaeBFMJixGSykuDjVoNx1YOhATxrODRUZ7snSn0szXm0Rxh4yhkdzJUZ7Mpzpy5JNWVj6CM9dXeDKgst0rY0QglrHR/clQcCKHJhiGvxIxw8lhJJOGOEY+i3FD3fqT9hMSO/tlFAcSy4tNKi0PJAaUpf4cUzdi5istAiimNS2Rrd3KOWhUNzBxLGkP+fwfY8cp+YP8w/eeoI/f/YiT18r88BAhu96x/2cGygQxDGz9Raff2mSP31mjNer23+mDmiaRiFt8a6zaaarHk3PI21aOIUAL5JIGeOGEgRYGuQdi1LG5FR/hnec7eP6XAMpBDKWZFMW/TmH/pxDTEx/zqHc6JDLmNTbIa1OhG3qWIYOEh483suZkYh2J0AXgoYfMrbQpO2GjM03mGuGeBGkTJMjxRRnh3JcLrfJZEwsXV+a3Hcjj2M3QnrjWFJ1fRbqHaIwpukFZB2TSEYIKbEM/dCE5y5HKQ+F4g5lrcmvP+tw35FHiWO5VLBxcRLrzToc7y3w1tNH+I9fusinXynf5glrIwQ8OFLANg0G8w7f97ajvDxeR9OTlryvT9aod3wa7ZCyG6BrGhnbYLiUIetY9GdTmJrOXCvA0QVZx6An45BPG0xW2jT8GBnHnB3MUW4HFFMGpqHRk7ZYaPrcM5QnYxn05WysbrJcOwiZKLepeR6vTDTphD5CGBwpONTckJ50klMDMFV1Gco7TNU7ty3Lfjt2GtLbCSImqy43ym3cUPLgaIn5dkCj42PrgvNHitw/XNhUNvx+o5SHQnEHst6K92gxtWSKMYV2y0SYtg3ecqKXMwM5Pjxd5isXJ/m9v5mmsYVnVzxAhvRkLYZLKTpBxKnBLLrQuDLb4NJsi9mGRyeAlAXHigZp2wSZZE7bls5RJ8NgKcIQGr1ZG10XTFZcerIOWquDbdrU3BAhBGnLZCifOJlHimESfdQ1Py3KWjJtSmmbIIoZymWYaXg4hkYYx0hcUrYO3Qq+4xWXlhcw3/Q53pfB0DUMXcMNgjVNWxuxk5Dexd+hLiBl6ThmiiiWvOtcH3PVDicHs5zqzXKkuPOM8r1AKQ+F4g5krRVv3fV4YaLK5bkmUkJf1ubC8RLFtLXis5omKGVt3nFqiLedHOTH3x3w6mSFqwtt/r9nx3hmrLVmVNPS54GZVoCpCa7Otphr+ElpEA3+4IlrdOKYbEonjiM6Psw0PCzTYK6lM1JKU0yZIAXVjiSSMFxIEUYxV2bbmEJSbkcU0gLLFPRnunWudI1YSkxDX5rcV5cfGS6mSNsGx3ozxBJmGx5eEBFFEj+IGV9oU/d8ZASaJqm0A2o3qjx0rIjsTuRCgt5NztuMCWsnIb2Lv8OUpaNrIinm2JPm3GCWuabPkbyDbRkMF1OHLtIKlPJQKO5IVq94/TBiruFRbvkUHBNdF7S8kBfGq7zjVN+aZo9FJ24xY/PW04O85ZTkPfcM8ltfvsSfPDXOwjptcW0ThnM2OcsgkzfJ2AZxHPPM2Dzj83WiOIl+imK6ZdChL5+i19GpuwHfnKygC52sY2DrOm6Q+DSG8jYZW+d4f4bpqosXSQbzDrqu4QbRiol5cdUexzE1N8APYqbrHS6MlkjbBmcHc4z2pLlRaXOiN81s0+PV6TovTtTIOybDRYfTgzkqTZ9Lsw3MbpXdbMpc04S1G9FU6/0O41jSl7WZrrkEoSSbszg9kE96xh+yxMDlKOWhUNyBrF7xxrEklzIot/2lLoiWkRRj9OMY4zZ93xYVyXApw8+9/34+cH6Yv319mr/6xhiXllVASevw4LEiDx/vwzENMo7J1fk6v/a5V3h2Yu2a8bU2XJyp0cil6Ask1CDb9VmcHykggCM5hyCMaQdJP47hnjSOrnF6IIepa7dMzJGUhF3FoQtBLmVSd30may6n+rJomsAwEnNUytQRLQ8/junP2ViGjhdKrs41OdWfxQtjwjim2gkxTR3H1PHCkCCK0aRY6hC5eoez1ve33d9hLGU3aMAmYxmHVmEsRykPheIOZfmKV0i4vtACCV4QoesCP4xJWSaWtjV7eTZl8uiJPh4+1sNH3nmGb4yX+fprs4zXOhwppnj0ZD9vP91H3Q355Nev8Sufvbyhmavsg9YKMOKARsdjKJ/CsQxCCZfnWxiGljj2u2U5NCEI45ggvKkwVk/MuhAgwQ9ict3dgmXoCMmSs3pxZe+FEe1OSLUdYBo6Lc8nMgw0TScIY7K2kfgUpGSu4TGYS3wnE1WXWCY7nJ60iRvGt+xwdsph6c2xHZTyUCjuYJZPrCM9adpByKvTjSWfx0NHi9uK1FmsAtxvpnj/PcN8y6lBvDAxHaUtA8PQqDZd/sPfbKw4Fpn3khd4TFY9LhzXydjJyFOWgWPptP2IIJJ4QchCy6eUMbm20FqzlLqmCYaLKabrHequj2XolNImuqYtOasXV/YT5TazLR8hoT9nQdZkvNIhZ5vousZIKQ3AbCO5V9bWEQjs7vcWxzEvTdUZKThkUxbNTrBih7NTDkNvju2glIdC8QbBMXXODxc5058jkhLH0HclxFPTBCnbuCXDebrWprNWtcLbUPbga5cWeNzs5+yQQ8FJzGxCQF/GYrrRYShnU/NCZmsez4yVuW8oT7rrPF5c8adtgwujJSZrLkIm5ct7syuDAxxT52hXqQ7kbK7Nt4ik5FgpxXvuHSAIJYYmCGO5FCmFhCCO8EJBw/V5caLKtQWX63mbE70ZjhRTK3Y4m2G/mkrtJ0p5KBRvIBYn+v1gpJTBMqCzjaoorRim6w2KGZPPvTyNbeqkLZPzIwWabohhamgCJqouddfnKXeBU/3ZW0xGadvgVF+Wlh8y1/CYa3gsCH9FtJSpa+Qci2LK4vRAlsCPELrGcCG91ENlouJi6oJT/Vmars8XX59FCpgquxiaQAcEMLbQoidtEpGYCjfDbjaVOkwo5aFQKLbFsd48P/DYCL/xpYltfb7a8rle7nBppknHjxjMO0RSknMswigi65j4UYQkCUV2wxjL0NY0GS00/W7Cn3Zru9YVjmkwTWMpasvRdEaKSahwzjGJpeTSfAtT07AMjXYn5Np8nVImRcPzGSllmKp59OccxqvubRXBbjaVOmwo5aFQKLaFpgl+8n3nmWt0+ORzC1v+/I1yjK61ODOQZUGDTiS5NNvk/ff18/x4G73ZwQ9ByphiyiIMIyQGMpIrTEabyfJe7ZgGCKIYvdt0yTJ0YplEp3l+RMY2yaQ0riw0uFFxmW2EpGxBpeXz9lM99OWdJcWwkSLYraZSh5HDl7aoUCjuGDKWwfe+7RTHslv/rA/MNzyuzNWpNHzaftK7PQZKGZPBnMOxnjQCwULLY74Z4IcRM02PILxZ2H15zguwbpb3YrkWP4oZK7e5UW4zVm7jRzFDBYcgkgRhjNAEKVMwNp8kQDqmRsrSiSSM11wcSyOKExNULBNFth6bHdudiFIeCoVi22ia4P6hEn/noaPb+nzZg1fnAiZrHSYrbWbrLlfnmmgIzg7lsA2NIyWHuhswmLcopG2GCw6zDY84lktjGMjZuEFE0w0IIrlulvdyM1LGNjB1wXStg6UnSYIjPWm+9dwACMFL4zUaboguJBlTUEqZEEmuLLS5OtfgufEy1XZnQ9/HosksiCQtL9xwbGuNNYjiJTkPG8pspVAodkQ2ZfKD7zjNF16e4HJ1exNdM0gcy0LWKWVsRnuz6BOCc0dyhKGNqWn0ZGyG8g6GrtHybpp+OkHEbCNJUJTAQM5e1w+xnhmp5YcsNH2iKKlq2+6E2Do0Q2jWJeN1l8E0DOYzXJqs8czlBdxAkrENWhciHj3Rt+4zt5LLsRiVtZiYeJid7GrnoVAodkQcS6brbWa3qTgWCQHHNDA1jSiKuDzfoB2EaLrGcCmdtLNlpeknDGPGK210ATnHJGXpK3Ylq8cZxxLRvQfdfwUw1/AIooiZRoeXJqtcnK7xpYsr/TgzbXjoaJaxskvDDxnI22Qsna9cnOf6QnPDHcLqCsdr0QkixsptxuZbPDtWIY7jFbujw7YDUTsPhUKxIyIpmW96tHbhXpZpoBkCNI28qdOXTkxVrU7AdMPD86OlwoV+FDNebjNRdcnYBv3dHcdaDunl4bJBGNMJIvTuhN6Xs7mx0OLyfAsZS/wIZpotqmvU9nr66hx9xRx9qQwIgQSiSNJygx05wVdHZWkCKm5AyjYOrZNd7TwUCsWO0IWgP2uv2Zt8KwggYxuEoURIyZnBLCC4MtdipuHTn7UZKDgcLaYQJH05bFMj6xhLpUX8IOlxu7jLWPx5tZ9jrukhY4kEhIT5po+MJbmUmeR1iLXX1WPlmGrTp+klGeuiK38mZe7ICX7TnJZkyFuGjh8k/o7D6mRXykOhUOwITROcHizw4MDOppOBrEHGNkibOvcMFXjsTB8py+BYT4ozA1nyKZPJqstYpc31hRbjFZe4W4ZFaEkV4XonWKpLNVZu0wmiFRNzLCUVN8DQkgnfNjSm6x16MhZCCNpeRCFlMVyw1zTL9GUFlmGQMgVNL8ALI955Tx/He3dWqmR5VJamCUppk0hK3CDakpN9P1FmK4VCsWPytsmH3n6Cb/7XK1v+bBF41309PHC0h3ee7cM0dUxdJ2MZVNsB6W4HQGKYrXuM9qRIpQ6+FksAAAsfSURBVEwqbZ+pqsvx3gwDOZucbaALgWPpK5IFjxZTK8Jl/SBeau2qaQJEhGPp3DeUY77l44Uh54Z7+bF36fzWl2+wWCv4RNHkniMlNBHxnnP9HOtNmjX15nY+sa+ukqxrGhdGS4e6LLtSHgqFYscYhsZ3P3yapy/X+dRL85v+nAb80PtO0p9Lc7wvQ18hjSaSXQSwomeJFyYmKcvUuxFIKcYW2jQ6AZahc6SYYq7hYXS77i36CqRgaWIO45hISgbSJprWvbemMZCzmW149GdtImkxkEvxzjN9vOlEgX//hcsEQcjx/hKljEXK1Ll3uMi9g7vbHvZOq7Ar5AYJLncCjzzyiHz66acPehgKhQIIw5jnJmf4D3/9Mn91qXPb6/+7tw3zoTeP0vJjRooOGScprx5EktGepPbUoqNbAH4Yk3WMpZ2FF8aMFFNLbVrHyu2u01lbcZ/lLWvXC4NdXrxw+XNvVJo8cWmBlhfimAbvvqefR070HrrQ2a0ghHhGSvnIju6hlIdCodht2l7I7339Vf7VX1xf95oP3JPlJ9/7APcPF4liuW5ew3qT+lr5D5stQriZKrcrnutH1P2AlKGTccxDvyu4HbuhPJTZSqFQ7Dpp2+DDb72X16c8Pvns9C3nL4yk+NkPPMSZwfxS58NRU19zQl/e78LRNjbtbNb0s5keGiue6xg4jpoul6O+DYVCsSekbYOPf88FPvKOOb7wyjRjs7NkM0UePdXLO8+MkFu1gt9sU6TbXXenNle601DKQ6FQ7BmaJnjg6AAPHB14QzZEuptRykOhUOwLakfwxkIlCSoUCoViyyjloVAoFIoto5SHQqFQKLaMUh4KhUKh2DJKeSgUCoViy9zxGeZCiDlg/TTW3aMP2HzRnjufu01euPtkvtvkhbtP5vXkPS6l7N/Jje945bFfCCGe3mk6/53E3SYv3H0y323ywt0n817Kq8xWCoVCodgySnkoFAqFYsso5bF5/v1BD2CfudvkhbtP5rtNXrj7ZN4zeZXPQ6FQKBRbRu08FAqFQrFllPJQKBQKxZZRymMZQoifEkI8LYTwhBC/verc24UQnxFClIUQc0KIPxJCHFnjHpYQ4lUhxPi+DXwH7ERmIcQvCCFeFEI0hBBXhRC/sO8CbIMdyiyEEB8TQix0Xx8XQhzqUrG3kdcSQnxSCHFNCCGFEI+vOm8LIX5TCDHT/U7+TAgxsp/j3w47kbl7zQUhxN8KIZpd2X92v8a+HXYq77LrNj13KeWxkkngXwK/tca5Eonz6QRwHGgAn1jjul8AZvdofHvBTmQWwA92r/sg8FNCiL+/l4PdJXYi80eB7wLeBDwEfCfw43s41t1gI3kBvgx8GLi15R/8LPAYiazDQBX41T0Y426zbZmFEH3Ap4F/B/QCZ4C/3pth7ho7+R0vsrW5S0qpXqte3V/Cb9/mmgtAY9Wxk8ArwLcD4wctx37IvOr8vwV+9aBl2UuZga8CH132/keAJw5alt2QFxgHHl917DeAjy97/x3Aawctyx7L/K+A3z3ose+XvN3jW5671M5j+7wbeGnVsV8FfhFw9384+8JaMgOJOQf4lvXO38Gslvk88Pyy9893j71R+Y/AO4UQw0KINPADwF8e8Jj2mrcDZSHEV4UQs11T3ehBD2qP2fLcpZTHNhBCPAT87yTbvMVjHwIMKeWfHNjA9pC1ZF7FL5H8f1rLlHdHso7MWaC27H0NyB52v8cOeB0YAyaAOnAf8MsHOqK95yjw35OY7EaBq8AfHOiI9pDtzl13jfIQQnyx6yxa6/XlLdznDMnK62ellF/qHssAHwd+em9Gvz32UuZV53+KxPfxHVJKb/ck2Dr7IHMTyC97nweasrv33292S94N+A3AIbH9Z4D/lwPeeeyDzC7wJ1LKp6SUHeCfA+8QQhR24d5bZi/l3cncddf0MJdSPr7TewghjgOfBf6FlPJ3l506S+Jg/VJ3AWoBBSHENPB2KeW1nT57O+yxzIvnfxj4n4F3SykPPMJsH2R+icRZ/vXu+zdxgKa63ZD3NrwJ+F+llGUAIcSvAr8shOiTUh5Iddp9kPkFYPliYPHnA9ld7rG825677pqdx2YQQhhCCAfQAV0I4QghjO65EeDzwK9JKX9z1UdfBI4BD3dfPwrMdH++sV/j3w47kBkhxA+QOBc/IKW8sp/j3gk7kRn4HeDnhRAjQohh4B8Dv71PQ98WG8nbPW93zwNY3fOLE+VTwA8KIQpCCBP4SWDyoBTHZtmhzJ8APiSEeLgr8z8FviylrO6rEFtgB/Juf+466OiAw/QisdvLVa9f6p77Z933zeWvde7zOHdItNVOZCaxBQerzv/mQcu0xzILkm1+ufv6ON0yP4f1tZG83fPX1jh/onuuF/h9khDOKknI51sPWqa9lLl7/idI/DwV4M+AYwct017Ku+y6Tc9dqraVQqFQKLaMMlspFAqFYsso5aFQKBSKLaOUh0KhUCi2jFIeCoVCodgySnkoFAqFYsso5aFQKBSKLaOUh+INixDit4UQf77Pz/yIEKK5h/dvCiE+slf3Vyg2i1IeCsXu8ofAqcU3QohfEkK8eIDjUSj2hLumtpVCsR9IKV3euCX5FYol1M5DcVfQre3zf4qkpWhHCPGEEOJdy84/3q1S+j4hxJNCiLZI2npeWHWfHxZCjHXP/5kQ4ieFEHLZ+SWzVde89M+A88uqoH6ke04KIb5n1b2vCSH+x2Xvz3QrqnaEEK8JIb5zDblGhBD/WQhR6b7+Qghxdne+NYVifZTyUNwtfBz4PuCHgTcD3wQ+LW7tQ/+vSaoEXwAWgN9fLJgnhHgM+H+AXyMpHPdfScp1r8cfAv8GeA040n394WYGK4TQgD8h+Rt9rDvuXwLsZdekgS8AHeA93eumgM92zykUe4YyWyne8HR7FvwE8KNSyr/oHvuHwHuBfwT8b8su/6dSyi90r/llkkKAIyTtO38G+Gsp5ce6174uhHgU+LG1niuldLu7kFBKuVHv6LV4P3A/cFJKOdYdz88By3uL/H2SQo0/JLtF6oQQP05SxPA7gf+yxWcqFJtG7TwUdwOnARP4yuIBKWUEfI1kgl7OC8t+nuz+O9D9915u9vFY5MndG+YK7gMmFhXHsmfFy96/haT3dKMbhdUk6WxYIpFZodgz1M5DcTew2KdhrRLSq48Fa5xbXGSJde6xHeSycS1iLvt5M42HNOA5kh3IasrbHJdCsSnUzkNxN3AJ8IHlDnKdxEfw8hbu8wrw1lXHVr9fjU/SoGc1cyQ+kMXxDC5/3x3XiBDi2KpnLf+bfRY4A8xLKS+teinlodhTlPJQvOGRUrZIenH/H0KIvyuEuK/7fhD49S3c6t8C3yaE+AUhxFkhxI8AH7rNZ64Bx4UQF4QQfUKIRYf354F/JIR4RAjxZpJuhJ1ln/ss8CrwO92Odo8BvwKEy675fZKub38qhHiPEOKkEOLdQoh/oyKuFHuNUh6Ku4V/QuJA/gSJqech4INSyqnN3kBK+TUS5/jPkPhGvgv4GCsn/dX8MfAp4HMku43v7x7/x8AV4IvAJ0miuGaXPSsmUUwaia/jd4B/CXjLrmkD7+7e549IlM1/IvF5VDYrl0KxHVQnQYViBwghfgV4v5TywYMei0KxnyiHuUKxBYQQvwB8hqS3+fuBfwj84oEOSqE4ANTOQ6HYAkKIPwQeBwrAVeDfAf+XVH9IirsMpTwUCoVCsWWUw1yhUCgUW0YpD4VCoVBsGaU8FAqFQrFllPJQKBQKxZZRykOhUCgUW0YpD4VCoVBsmf8fcSyMWPyr6NkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y='latitude', alpha=0.1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2450e195320>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y='latitude', alpha=0.4, s=housing['population']/100, label='population', c='median_house_value', cmap=plt.get_cmap('jet'), colorbar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924485</td>\n",
       "      <td>-0.101818</td>\n",
       "      <td>0.038676</td>\n",
       "      <td>0.063064</td>\n",
       "      <td>0.094276</td>\n",
       "      <td>0.049306</td>\n",
       "      <td>-0.017040</td>\n",
       "      <td>-0.046349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924485</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005296</td>\n",
       "      <td>-0.029224</td>\n",
       "      <td>-0.059998</td>\n",
       "      <td>-0.102499</td>\n",
       "      <td>-0.064061</td>\n",
       "      <td>-0.076571</td>\n",
       "      <td>-0.142983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.101818</td>\n",
       "      <td>0.005296</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.360922</td>\n",
       "      <td>-0.320624</td>\n",
       "      <td>-0.292283</td>\n",
       "      <td>-0.302796</td>\n",
       "      <td>-0.121711</td>\n",
       "      <td>0.103706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.038676</td>\n",
       "      <td>-0.029224</td>\n",
       "      <td>-0.360922</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930489</td>\n",
       "      <td>0.857936</td>\n",
       "      <td>0.920482</td>\n",
       "      <td>0.198268</td>\n",
       "      <td>0.133989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.063064</td>\n",
       "      <td>-0.059998</td>\n",
       "      <td>-0.320624</td>\n",
       "      <td>0.930489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878932</td>\n",
       "      <td>0.980255</td>\n",
       "      <td>-0.009141</td>\n",
       "      <td>0.047980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.094276</td>\n",
       "      <td>-0.102499</td>\n",
       "      <td>-0.292283</td>\n",
       "      <td>0.857936</td>\n",
       "      <td>0.878932</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907452</td>\n",
       "      <td>0.004122</td>\n",
       "      <td>-0.026032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.049306</td>\n",
       "      <td>-0.064061</td>\n",
       "      <td>-0.302796</td>\n",
       "      <td>0.920482</td>\n",
       "      <td>0.980255</td>\n",
       "      <td>0.907452</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012776</td>\n",
       "      <td>0.063714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.017040</td>\n",
       "      <td>-0.076571</td>\n",
       "      <td>-0.121711</td>\n",
       "      <td>0.198268</td>\n",
       "      <td>-0.009141</td>\n",
       "      <td>0.004122</td>\n",
       "      <td>0.012776</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.690647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.046349</td>\n",
       "      <td>-0.142983</td>\n",
       "      <td>0.103706</td>\n",
       "      <td>0.133989</td>\n",
       "      <td>0.047980</td>\n",
       "      <td>-0.026032</td>\n",
       "      <td>0.063714</td>\n",
       "      <td>0.690647</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924485           -0.101818     0.038676   \n",
       "latitude            -0.924485  1.000000            0.005296    -0.029224   \n",
       "housing_median_age  -0.101818  0.005296            1.000000    -0.360922   \n",
       "total_rooms          0.038676 -0.029224           -0.360922     1.000000   \n",
       "total_bedrooms       0.063064 -0.059998           -0.320624     0.930489   \n",
       "population           0.094276 -0.102499           -0.292283     0.857936   \n",
       "households           0.049306 -0.064061           -0.302796     0.920482   \n",
       "median_income       -0.017040 -0.076571           -0.121711     0.198268   \n",
       "median_house_value  -0.046349 -0.142983            0.103706     0.133989   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.063064    0.094276    0.049306      -0.017040   \n",
       "latitude                 -0.059998   -0.102499   -0.064061      -0.076571   \n",
       "housing_median_age       -0.320624   -0.292283   -0.302796      -0.121711   \n",
       "total_rooms               0.930489    0.857936    0.920482       0.198268   \n",
       "total_bedrooms            1.000000    0.878932    0.980255      -0.009141   \n",
       "population                0.878932    1.000000    0.907452       0.004122   \n",
       "households                0.980255    0.907452    1.000000       0.012776   \n",
       "median_income            -0.009141    0.004122    0.012776       1.000000   \n",
       "median_house_value        0.047980   -0.026032    0.063714       0.690647   \n",
       "\n",
       "                    median_house_value  \n",
       "longitude                    -0.046349  \n",
       "latitude                     -0.142983  \n",
       "housing_median_age            0.103706  \n",
       "total_rooms                   0.133989  \n",
       "total_bedrooms                0.047980  \n",
       "population                   -0.026032  \n",
       "households                    0.063714  \n",
       "median_income                 0.690647  \n",
       "median_house_value            1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "imputer = Imputer(strategy='median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num = housing.drop('ocean_proximity', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.1851e+02,  3.4260e+01,  2.9000e+01,  2.1290e+03,  4.3700e+02,\n",
       "        1.1670e+03,  4.1000e+02,  3.5458e+00,  1.7985e+05])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.1851e+02,  3.4260e+01,  2.9000e+01,  2.1290e+03,  4.3700e+02,\n",
       "        1.1670e+03,  4.1000e+02,  3.5458e+00,  1.7985e+05])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'median'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, ..., 0, 0, 3])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoded = encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['<1H OCEAN' 'INLAND' 'ISLAND' 'NEAR BAY' 'NEAR OCEAN']\n"
     ]
    }
   ],
   "source": [
    "print(encoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:371: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1, 1))\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.int32'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer(sparse_output=True)\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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